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PETBioNewsNewsPredicting patients benefits from new ovarian cancer treatment

BioNews

Predicting patients benefits from new ovarian cancer treatment

Published 20 March 2020 posted in News and appears in BioNews 1040

Author

Jennifer Frosch

Image by Peter Artymiuk via the Wellcome Collection. Depicts the shadow of a DNA double helix, on a background that shows the fluorescent banding of the output from a DNA sequencing machine.
CC BY 4.0
Image by Peter Artymiuk via the Wellcome Collection. Depicts the shadow of a DNA double helix, on a background that shows the fluorescent banding of the sequencing output from an automated DNA sequencing machine.

Analysing the tumour genome and cancer microenvironment can predict an ovarian cancer patient's response to a novel immune-PARP combination treatment, according to a new study...

Analysing the tumour genome and cancer microenvironment can predict an ovarian cancer patient's response to a novel immune-PARP combination treatment, according to a new study.

Previous clinical trials testing a combination of drugs known as immune checkpoint inhibitors and PARP inhibitors can produce strong remissions, leading to complete or partial shrinkage of tumours in 18 percent of patients with so-called 'platinum-resistant ovarian cancer', which is cancer that reappears within six months of completion of multiple-lines of chemotherapy.

There was previously no way to predict which patients would benefit from the experimental treatment in advance. Now, researchers from the Dana-Farber Cancer Institute in Boston, Massachusetts have identified two key factors that are able to predict a beneficial response to a therapeutic approach currently in clinical development for the treatment of patients with advanced ovarian cancer. These two factors are: a DNA repair-related mutation and evidence of involvement of the immune system in the cancer environment.

'By taking these factors into account, researchers leading trials of this combination in patients with advanced, chemotherapy-resistant ovarian cancer may select individuals who may respond to this combination of drugs,' said co-senior author Dr Panagiotis Konstantinopoulos.

The study, published in Nature Communications, characterised tumour samples from 62 individual patients enrolled in a Phase I/II trial of the drug combination.

Comparing immunogenic profiles and imaging data, the authors identified two prognostic features, and discovered that patients whose cancer tissue had one or both of these features were more likely to experience positive effects from the drug combination than those without.

Of the patients with these features, 18 percent had an overall response where the tumour size decreased, including three patients with complete clearance. Furthermore, in 65 percent of patients the drug combination stopped tumour growth, keeping the disease under control.

'Patients with advanced or metastatic ovarian cancer who are resistant to standard platinum-based chemotherapy agents often have few further options for treatment,' Dr Konstantinopoulos commented. 'Our findings will help ensure that patients for whom a PARP inhibitor-checkpoint inhibitor combination won't be beneficial can focus on other clinical trials of treatments that may be more effective for them.'

Sources and References

  • 19/03/2020
    Eurekalert!
    New research shows which ovarian cancer patients won't benefit from immune-PARP combo
  • 19/03/2020
    Nature Communications
    Immunogenomic profiling determines responses to combined PARP and PD-1 inhibition in ovarian cancer
  • 19/03/2020
    Precision Oncology News
    Ovarian cancer mutational signatures, immune features tied to combination treatment response

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Image by Peter Artymiuk via the Wellcome Collection. Depicts the shadow of a DNA double helix, on a background that shows the fluorescent banding of the output from a DNA sequencing machine.
CC BY 4.0
Image by Peter Artymiuk via the Wellcome Collection. Depicts the shadow of a DNA double helix, on a background that shows the fluorescent banding of the sequencing output from an automated DNA sequencing machine.
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