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‘Classic Bedside-to-Bench-and-Back Research’

2015· article· en· W2548306309 on OpenAlexaboutno aff
Robert H. Carlson

Bibliographic record

VenueOncology Times · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Operations and Scheduling Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsBench to bedsideComputer scienceMedicineMedical physics

Abstract

fetched live from OpenAlex

FigurePHILADELPHIA—Research translated from bedside to bench and back resulted in a novel combination of the dual mTOR-C1/C2 inhibitor AZD2014 and paclitaxel being tested in ovarian and lung cancers. An observation from cells isolated from the ascites of patients with ovarian cancer led to a Phase Ib clinical trial that was highlighted in a news conference here at the American Association for Cancer Research Annual Meeting, due to the promise in heavily pretreated patients. Three of the seven patients with ovarian cancer have had partial responses, and two of five with squamous non-small cell lung cancer (NSCLC) pretreated with docetaxel had partial responses (Abstract CT138), reported Udai Banerji, MD, PhD, Team Leader and Reader in Molecular Cancer Pharmacology at the Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. Banerji said the earlier research showed that cancer cells isolated from ascites samples of patients with ovarian cancer who went on to receive chemotherapy had elevated levels of p-S6 kinase in the cancer cells. The elevated p-S6 kinase was associated with chemoresistance), and the researchers hypothesized that combining chemotherapy with a novel dual mTORC1/2 inhibitor would be effective in this setting. “Our study is a classic example of a bedside to bench and back to bedside story,” Banerji said. Recommended Dose The recommended dose moving into Phase II testing for AZD2014 was 50 mg twice daily on either two or three days a week, and paclitaxel at 80 mg/m2 a week. The trial, which was funded by AstraZeneca, was unusual in that AZD2014 was started at the maximally tolerated dose of 50 mg twice daily, Banerji said. “We fail quite badly when we try combinations of chemotherapy with targeted signaling agents, and one reason is that when we try to combine small molecules with every-three-week chemotherapy we have a pharmacokinetic interaction of only approximately 24 to 36 hours. We decided to use a weekly chemotherapy—a very easy fit because ovarian cancer treatment already includes weekly paclitaxel.”FigureA second reason for failure of combinations of chemotherapy with targeted signaling agents is toxicity: “It is very toxic to give both drugs continuously, so we saved ourselves and our patients a lot of trouble by not going past three days of AZD2014.” To date, seven of the 10 ovarian cancer patients available for evaluation who completed one cycle of treatment have achieved a partial response by RECIST criteria, he reported. “Our gynecological-oncology colleagues tell us that a response rate of 30 to 40 percent is good, so seven out of 10 patients is a good place to start.” Two patients with squamous NSCLC in the dose-expansion section of the study had partial responses, but paclitaxel was discontinued in the NSCLC patients at seven months because of peripheral neuropathy, a result likely due to their prior taxane treatments. “An expansion cohort in squamous lung cancer is currently ongoing to follow up on these encouraging findings. We will also be launching a randomized Phase II trial in the United Kingdom to test the combination in patients with advanced ovarian cancer, and we will be recruiting patients to an open-label Phase Ib multicenter trial to test the combination in patients with squamous NSCLC.” Discussant's Remarks The Discussant for the study, Philippe Bedard, MD, Assistant Professor of Medicine at the University of Toronto and Princess Margaret Cancer Centre in Toronto, said the real message of the study is that the pharmacokinetics of the compound were very similar to what was seen in the monotherapy experience—i.e., very rapid time to maximal or peak concentration and rapid clearance of the drug and a short half-life. And although the data are few, the trial's response rates are an encouraging signal of some combination activity, he said. But he pointed out that there were no tumor biopsies reported in order to confirm the pharmacokinetic combination effects seen in preclinical models. And although Bedard commended the authors for trying to seek out predictive biomarkers, there still are no predictive biomarkers for rapalogs or TORC1 or TORC2 inhibitors in the clinic—“or even taxanes, for that matter,” he said. “It's very difficult in a single-arm Phase I expansion cohort to really see a clear signal in terms of a predictive biomarker for the combination, particularly when monotherapy or chemotherapy alone has some activity.” Further, he said it is not clear from this trial what the optimal schedule must be to see a combination effect, or how long TORC1 and TORC2 have to be inhibited in order to see some combinatorial effect. Finally, he questioned whether the effect was due to synergy or additivity. “One of the big challenges we face with the increasing number of targeted drugs that are available in the clinic, there are a growing number of possible combinations, but many more than can practically be explored,” Bedard continued. “We need some system to prioritize the combinations that should be taken forward, to identify the best combinations to proceed to clinical evaluations.” Bedard applauded the researchers for designing the trial with an underlying hypothesis that justifies a combination study in patients, which follows the National Cancer Institute and Investigational Drug Steering Committee (IDSC) guidelines for Phase I combinations (Paller et al: Clin Cancer Res 2014;20:4210-4217). He commended the team “for all of the studies that they did leading up to this very encouraging clinical trial. This illustrates a nice rational progression in terms of the story—from the clinical observation to translational science to ultimately an investigator-initiated Phase I clinical trial that's leading to additional studies.” Mechanism of Action Banerji explained that AZD2014 is a dual mTOR (mammalian target of rapamycin) inhibitor that targets both TORC1 and TORC2, making it possible to reverse the effects of p-S6 kinase, which is associated with chemo-resistance. The mTOR inhibitors are thought to work by anti-angiogenesis, and the mechanism of paclitaxel is thought to be partially due to its anti-angiogenic properties. The explanation, therefore, was that the combination of AZD2014 and paclitaxel would have synergistic angiogenic effects. “We put the combination through two very chemotherapy-resistant models, ovarian and NSCLC, and it looked good,” Banerji said. AZD2014 Combined with Fulvestrant in Breast Cancer Another highlighted AZD2014 study at the meeting used the drug in combination with fulvestrant for women with estrogen receptor-positive metastatic breast cancer (Abstract CT233). There were 66 patients in total, 43 treated on a continuous schedule of AZD2014 twice daily, and 23 on an intermittent schedule of AZD2014 twice daily on two days a week. Among the evaluable patients, 46 percent who had dosing continuously had a clinical benefit, as did 33 percent of patients treated intermittently, said the study's first author, Manish R. Patel, MD, Associate Director of Drug Development for Sarah Cannon Research Institute and Director of Drug Development at the Florida Cancer Specialists and Research Institute. He said that clinical data from the BOLERO 2 trial suggest that resistance to endocrine therapy in breast cancer is associated with activation of the mTOR intracellular signaling pathway. As a selective dual mTORC1 and mTORC2 inhibitor, AZD2014 may offer additional benefit over mTORC1 inhibitors through suppression of the AKT pathway via mTORC2. AZD2014 was administered on a continuous or intermittent schedule, and fulvestrant at 500 mg was given intramuscularly on day 1 of each 28-day cycle. “While the continuous and intermittent dosing schedules appear to be equally effective in ER-positive breast cancer, the latter may achieve improved tolerability in patients,” Patel said. Indeed, the intermittent schedule had a different adverse events profile, with a lower incidence of rash. Common side effects for both groups were rash, nausea, vomiting, diarrhea, sore mouth, and fatigue. He noted that the ongoing randomized Phase II MANTA trial is assigning patients to fulvestrant with either of the AZD2014 dosing schedules or to fulvestrant with another mTOR inhibitor, everolimus. AZD2014 differs from everolimus, which only partially inhibits mTORC1, he explained. Genome or Proteome? The confusing patterns generated by next-generation sequencing in the attempt to identify biomarkers for targeted therapies prompted the Discussant for the presentation, Matthew W. Ellis, MD, PhD, Professor and Director of the Breast Care Center at Baylor College of Medicine, to question whether researchers are looking in the wrong direction. “It's hard for me to see how a composite multi-gene panel can be generated from this information,” he said. “You have to ask yourself, are we barking up the wrong tree with genomics and these drugs, where the genomic aberrations that generate the dependency are very heterogeneous and complex? “Perhaps we are measuring the wrong thing—perhaps instead of measuring the genome we should be measuring the direct targets of these drugs—in other words, the kinases and their activity. Or to put it another way, we are drugging the proteome and not the genome and therefore we should measure the proteome, or more specifically, the ‘kinome.’” Ellis suggested that proteomics or “proteogenomics” might improve the precision to predict the efficacy of these drugs. As an example of research heading in the wrong direction, he pointed to the exploratory genetic analysis of the BOLERO-2 trial in search of a biomarker of benefit from the mTOR inhibitor everolimus in breast cancer. That retrospective exploratory analysis found no predictive marker of everolimus efficacy in subgroups defined by each of the four most frequently altered genes and pathways when assessed individually.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.401
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.008

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.334
GPT teacher head0.558
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2015
Admission routes1
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