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Abstract A20: Enhancing checkpoint inhibitor therapy with ultrasound stimulated microbubbles

2020· article· en· W3031072535 on OpenAlexaff
Sharshi Bulner, Aaron Prodeus, Jean Gariépy, Kullervo Hynynen, David E. Goertz

Bibliographic record

VenueCancer Immunology Research · 2020
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsSunnybrook Hospital
Fundersnot available
KeywordsMedicineMicrobubblesUltrasoundRadiation therapyImmunotherapyChemotherapyColorectal cancerCancer researchCancerPharmacologyOncologyInternal medicineRadiology

Abstract

fetched live from OpenAlex

Abstract Checkpoint inhibitor (CI) therapies are playing an increasingly prominent role in the treatment of cancer but are only effective and durable in subsets of patients. This has prompted the investigation of combining multiple CIs or their use in conjunction with conventional therapies such as chemotherapy to achieve complementary effects. A significant consideration with combinatorial approaches is the associated increase in toxicity. This provides a compelling motivation to couple CI therapy with locally applied physical methods such as radiotherapy or therapeutic ultrasound. Therapeutic ultrasound is undergoing rapid development for oncological applications and can elicit therapeutically relevant effects through ablation or impacting vascular function. It is also known to evoke immune responses, though their use in combination with immunotherapy remains to be established. One therapeutic ultrasound approach is to acoustically stimulate systemically injected “microbubbles” to undergo violent oscillations in targeted tumor regions to shut down the vasculature. We have previously shown that this approach can potentiate the antitumor effects of a range of chemotherapeutic agents. In the present study, we investigate if this form of “antivascular” ultrasound can enhance the efficacy of anti-antagonistic PD-1 (aPD-1) therapy. To test this paradigm, we used a mouse colorectal cancer line (CT26.wt), which was initiated subcutaneously in the right hind limb of 8- to 12-week-old female Balb/c mice. Mice were split into four groups: MBs (control), aPD-1 (drug group), US (ultrasound plus microbubbles group) and US + aPD-1 (combo group). Experiments were initiated on mice when tumors were in the range of 50-100 mm3. The immunotherapy drug used in this study is an anti-mouse PD-1 (clone: RMP1-4, Bioxcell). The drug aPD-1 was administered intraperitoneally at a dosage of 200 μg to respective groups prior to treatment and subsequently administered every 3 days for a total of 5 doses. Longitudinal studies were done where tumor growth was monitored every 3 days until animals reached endpoint (tumor size>1000 mm3). Acute experiments were done, which included flow cytometry and ELISPOT to assess how T-cell populations/activity changed with each respective treatment. Longitudinal experiments (n=5-6) showed that US + aPD-1 treatment significantly inhibited tumor growth relative to MB-only, US-only group and aPD-1 group at Day 6 (p<0.0001, p<0.01, p<0.01, respectively) and at Day 9 (p<0.0001, p<0.01, p<0.05, respectively). This inhibition of tumor growth with combinatorial treatment translated to longer survival times compared to MBs (p<0.01), US (p<0.01) and aPD-1 (p<0.005). Flow cytometry (n=5-7) and ELISPOT (n=6) data did not clearly show a T cell-dependent mechanism for the inhibition of tumor growth. In conclusion, these results demonstrate the ability of “antivascular” US therapy to enhance CI therapy, while the specific mechanisms of enhancement remain to be elucidated. Citation Format: Sharshi Bulner, Aaron Prodeus, Jean Gariepy, Kullervo Hynynen, David E. Goertz. Enhancing checkpoint inhibitor therapy with ultrasound stimulated microbubbles [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2018 Nov 27-30; Miami Beach, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(4 Suppl):Abstract nr A20.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.834

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.312
Teacher spread0.258 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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
Published2020
Admission routes1
Has abstractyes

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