This study led us to the discovery of 53 gene signatures called IMPRINT and with the data we used to develop the signature. We could prove that they could identify patients’ HER2 positive and HER2 negative that could benefit from PD-1 inhibitors. We run a sub-analysis of an I-SPY dataset I-SPY2 is a phase 2 adaptive clinical trial.
And we did validate our findings on our independent test set. And when we validate the accuracy of our signature in predicting patient that respond to immunotherapy. We could see that the senior tool had a very high, positive, predictive values about 77% in the discovery set and even higher to 95% in the validation set, especially in HER2 negative HR, positive patients early breast cancer patients.
So, this signature is very effective in this type of early breast cancer tumor. This HR positive hurt negative. That usually are not the one that are actually under investigation in many of the immunotherapy trials that are currently ongoing.
The main question was asked to ask to the whole development team, right? Because this is a very large team effort in collaboration. Also, with I spoke to biomarker working group was why we need such a signature. What’s the added value over currently available biomarkers? So why we need that is because currently the biomarkers that are available for identified patient that might bene benefit, sorry from immunotherapy are unsatisfactory, or at least they show.
A lot of variability in term of positive, predictive value in terms of performance. To, we try to really develop a signature that could capture the entire Spectrum or alteration of immune sensitive tumors instead of using a single biomarker, which currently for example, is the case for PDL1 or PD1 we decide to go for a Multigene biomarker.
So, this tool includes 53 genes and is run on a micro rate platform. We also put that’s robust in the way our is let’s say defining or identifying the immune positivity phenotype because this signature is consistent. In a way that is run on a central micro race testing is highly reproducible.
So, if we would repeat the testing one, two, many times we would get the same results. Being multiple gene and being consistent make this singer to a unique, let’s say biomarker that was not yet out there to enable a better certification and a better identification of patient that could really benefit from immunotherapy.
Also, because immunotherapy is high cost and also unfortunately many side effects. So, you don’t want to treat everyone. You really want to find those that benefit from treatment. And this is a way to get close to so that to be able to identify those, that benefit the most.
This data doesn’t affect clinician today. As now we speak as because this has been now currently prospectively validated. In the framework of the I-SPY2.2 trial therefore cannot be used standardly in the clinic yet, but what is actually affecting clinician now? What is the say added value of the discovery at this point is really highlights the complimentary and value and added value of genomic?
To what is the mostly the standard clinical pathological assessment. So, it’s really what’s changed now. I would, it helps changing the mindset from using standard clinical pathological assessment and adding genomics to it. So, as I just, you mentioned Just in the beginning. So, imprint has been discovered and independent validated in IPI two trial data, and now has been prospect validated also in the I-SPY2.2 trial.
So really, we will be able to see whether this signature has predictive value in identify a patient that could be randomized to immunotherapy targeted agents.
What I want to highlight. More is that this signature will be, yeah. Prospectively validated in I-SPY2.2, but not a standalone biomarker. So, it will imprint. That’s the name of the product of the signature actually will be used together with other biomarkers. For example, ma print and blueprint which are.
The 70 gene recurrence test as say, and the, at gene molecular subtyping assay these are already being used in ISPI, but what is the novelty is that imprint together with normal print blueprint and other biomarkers will be used to define what is now currently called also within IP. Response predictive subtype.
So, a patient will be assigned to a specific response, predictive subtype. And based on this response, predictive subtype patient will be randomized to the treated treatment or a targeted treatment that we predicted would benefit. We predict the patient will get the best benefit out of it. So, it’s really in the contest of multiple biomarkers, we imprint will be used.
What they need to know is that this is we were able to let’s say, identify such a signature because we had stronger expression data or genomics data that could allow us to really identify the best predictive genes to then made up the now so- imprint singer. But also, we (inaudible) course clinical information and very well curated data, clinical data.
So, to come up with such a discovery you need to have good genomics data, good clinical data and also decent number to be able to identify, to have robust the findings. And of course, This discovery is in a, in the context of a large say clinical trial, which is I-SPY2. And we could also make use of say data that were generated in the past to further validate, further assess this I think data quality availability of clinical information is key to come up with such a discovery in this case with this senior.
Lorenza Mittempergher, PhD, Senior Director Research at Agendia, Biomedical Scientist. In this video, she speaks about the ASCO 2022 Abstract – The ImPrint immune signature to identify patients with high-risk early breast cancer who may benefit from PD1 checkpoint inhibition in I-SPY2.
Origins:
The astonishing surge in new immuno-oncology medications in many cancers has necessitated the development of biomarkers to identify who will benefit. Although several predictive biomarkers (PD-1/PD-L1 expression, alterations in mismatch repair genes and microsatellite instability, tumor mutational burden, and immune infiltration) have been established, none have consistently predicted efficacy. The I-SPY2 consortium validated various immune biology-related expression-based markers that predict sensitivity to PD1 checkpoint inhibition. We examined whole transcriptome data from high-risk early breast cancer (EBC) patients who received Pembrolizumab as part of the neoadjuvant biomarker-rich I-SPY2 trial (NCT01042379), with the goal of translating I-SPY2 research findings into a robust clinical grade platform signature to predict sensitivity to PD1 checkpoint inhibition.
Methodology:
Pre-treatment biopsies from 69 HER2- patients recruited in the I-SPY2 trial’s Pembrolizumab (4 cycles) arm yielded whole transcriptome microarray data. All patients had a High-Risk MammaPrint profile with 70 genes. At the time of surgery, pathologic complete response (pCR) was defined as no remaining invasive cancer in the breast or nodes. Thirty-one patients (31 HR (hormonal receptor)+HER2-, 19 Triple Negative (TN)) had a pCR, while 38 (28 HR+HER2-, 10 TN) had residual disease (RD). Gene selection was performed comparing pCR and RD groups by iteratively partitioning the dataset in training and test, balancing for HR status, to identify the best predictive genes related with pCR. Due to the small sample size, leave one out cross validation was employed to evaluate performance. Genes having an effect size greater than 0.45 were considered significant.
Outcomes:
ImPrint, a signature of 53 genes, was discovered with overall sensitivity and specificity greater than 90% and greater than 80% for predicting pCR to pembrolizumab in all patients. Sensitivity and specificity in TN were greater than 95% and 70%, respectively, and in HR+HER2- greater than 80% and >85%, respectively. The Positive Predictive Value (PPV) for the HR+HER2- subgroup is 77%. The biological annotation of the 53 genes revealed that more than 90% of the genes have known immune system functions, with 63 percent previously recognized to be involved in immune response (including genes coding PD-L1 and PD-1, as well as those identified in I-SPY2).
Findings:
ImPrint predicts pCR to Pembrolizumab in a group of 69 high risk EBC with high sensitivity and specificity during the signature development phase. The signature contains genes with immune-related functions that are known to be important in immune response, implying that it could aid in the identification of patients with an immunological-active phenotype. ImPrint appears to be helpful at identifying a fraction of HR+HER2- patients who may benefit from immunotherapy. External validation in independent dataset(s) is now underway and will be presented during the symposium.