6-factor prognostic model for patients with advanced urothelial carcinoma

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Published: 9 Feb 2018
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Dr Gregory Pond - McMaster University, Hamilton, Ontario

Dr Pond speaks with ecancer at the 2018 ASCO Genitourinary Cancers Symposium about a new 6-factor prognostic model for patients with advanced urothelial carcinoma receiving post-platinum atezolizumab.

The model is based on two previous clinical trials using atezolizumab.

Six factors were deduced which were prognostic for survival.

The adverse risk factors for each of the six were identified (risk stratification) and quantified for each patients.

This was then evaluated against the second previous clinical trail dataset.

He found that those with 4 or more risk factors had a median survival of less than 3 months - which means those patients may not benefit from immunotherapy, an important factor to identify. 


checkpoint inhibitors which have been approved by the US FDA for patients who have advanced urothelial carcinoma who have failed on previous platinum-based chemotherapy. One of these treatments is atezolizumab. So the problem that we have is that we don't know which patients are going to respond to therapy and which ones are not going to respond to therapy. So we need to develop these models in an attempt to identify patients that would best respond to therapy and which patients may have to get some other therapy. So that's the background for the study that we looked at.

We took two previously published clinical trials using atezolizumab and we developed the models based on these two clinical trials. The first trial was the IMvigor210 clinical trial. Now this was a dataset containing 310 patients and we used this as training our development dataset. What we did is in this dataset is we used Cox regression models, which is a statistical technique, to identify those factors which were potentially prognostic of overall survival. Then we developed an optimal model using something called forward selection and based on this model we found that there were six factors we identified which when put together were prognostic for survival. Then for each of those six factors we identified what were the adverse risk factors and it's basically called risk stratification and listed the number of risk factors that each patient had. So then we took that risk stratification and put it into our validation dataset which was the PCD4989g study, that was a phase Ia study of 95 patients, and we evaluated the performance of the model in that validation dataset. What we found was that those patients who had zero or one of these risk factors performed fairly well and had a median survival of close to 20 months. However, those patients who had four or more of these risk factors had a median survival of only less than 3 months. Now this would be very important if this holds true because those patients, there's a belief that patients who have an expected survival of 3 months or poor may not benefit from immunotherapy. So it would be very important for us to identify if this is true in a further dataset. Given the datasets that we had, and the validation dataset was only a small dataset of only about 95 patients, we want to expand on that and look at a larger dataset as well as to see how the model performs in other checkpoint inhibitors.

So the six factors - neutrophils-lymphocyte ratio; ECOG performance status; LDH; anaemia; liver metastases and platelet count are the six factors.