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Record W7161967392 · doi:10.82308/8322

PD-L1 immune checkpoint inhibition in combination with radiation across different bladder cancer molecular subtypes and influences on immune memory

2021· dissertation· en· W7161967392 on OpenAlexaboutno aff
JiaMin Huang

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsBladder cancerAbscopal effectImmunotherapyImmune systemRadiation therapyImmune checkpointAntigenCancer

Abstract

fetched live from OpenAlex

Bladder cancer is the 5th most prevalent cancer in Canada. 30% of the cases are muscle-invasive (MIBC). MIBC is a highly lethal disease; the patient survival rate has not improved over the last decades, and treatments option is limited. The standard gold treatment is radical cystectomy; however, more than 40% of the patients are unfit to undergo surgery. Hence, new strategies to improve treatments for MIBC are imperative. Radiotherapy (RT) is an alternative treatment and allows bladder preservation. Although it maintains the quality of life, half of the patients will develop metastasis. RT can enhance tumor antigen presentation, but it can also induce a higher expression of programmed-cell-death1(PD-1) on T cells and programmed-cell-death-ligand1(PD-L1) on cancer cells. This interaction causes T cell exhaustion and impairs the immune response against cancer. The effect can be prevented by the addition of anti-PD-L1 molecules, which will result in an increase of infiltrating T cells. Thus, combining immunotherapy with RT may boost the systemic immune response leading to improving oncological outcomes of bladder preserving strategies. Our lab has previously shown an abscopal response when treated with the combined treatment, which proved the efficacy of the combination. However, immunological memory is required for durable response, which could prevent metastasis and recurrence. We hypothesize that the combination of radiotherapy and immunotherapy will generate the development of immunological memory, which is needed for long-lasting responses. To analyze the different responses to the combinational treatment, we used different murine bladder cancer cell lines and characterized the tumor microenvironment and the immune memory profile. MB49 represents the basal molecular subtype and a hot tumor model. UPPL represents the luminal molecular subtype and a cold tumor model. The baseline characterization of the immune response was compared between the two molecular subtypes of bladder cancer. MB49 had a higher T cell infiltration compared to UPPL. UPPL had a higher infiltration of neutrophils. The induction of memory was assessed by comparing the memory populations across treatment groups: control, RT, anti-PD-L1 and RT + anti-PD-L1. Following treatment, the tumors were excised via surgery to create a tumor-free, tumor antigen encountered mice model. Mice deemed tumor-free after 5 weeks without tumor growth in the right flank were re-challenge with a second cancer cell injection in the opposite flank. This tumor re-challenge growth kinetics determined the memory response. The combined treatment was expected to produce a larger and effective pool of memory T cells compared to other treatment groups by generating long-term immune memory, which would correlate with either tumor rejection or growth delay in the re-challenge. A distinct immune profile was expected in response to combination therapy across the molecular subtypes. Findings from this study will have a very important clinical relevance as it provides evidence for a more sustained clinical response using combination therapy across different molecular subtypes. Importantly, the potential of eliminating micrometastatic disease using augmented systemic effects of the combined approach is highly promising

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.289
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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