Identifying Immunotherapy Responders Using Digital Pathology Methods

Have you ever considered how the TNM (tumor-nodes-metastases) staging system doesn’t take the immune system into account? Still widely used in the day-to-day practise of most oncologists, this classification of a patient’s cancer stage has not kept up with the rapid developments in immuno-oncology and immunotherapy. Today, immune evasion is even one of the updated hallmarks of cancer, so certainly, a patient’s immune status needs to be taken into account to accurately provide a prognosis and survival prediction. In the early 00’s these observations sparked the mind of award-winning French biologist Jérôme Galon as he set out to develop the Immunoscore®.

What is the Immunoscore®?

In his early research, Galon studied the immune contexture (i.e. the type, density, functional immune orientation, and location of immune cells in a tumor) in colorectal cancer patients and the effect that the immune contexture has on the patients’ outcomes. Eventually, this led to the development of an immunohistochemistry- and digital pathology-based assay, which allows the quantification of two T cell subsets (CD3+ and CD8+) within tumors. The Immunoscore® was born.

The Immunoscore® grades the densities of CD3+ and CD8+ T cells in the centre of a tumor and at the invasive margin. The scores range from Immunoscore 0 for tumors with a low lymphocyte density in both areas to Immunoscore 4 for tumors with a high lymphocyte density in both areas. A high score indicates an inflamed tumor with strong pre-existing adaptive immunity. On the opposite, a non-inflamed tumor with weak or absent pre-existing adaptive immunity typically gets a low score.

To obtain an Immunoscore®, two adjacent slides of the patient’s formalin-fixed, paraffin-embedded tumor blocks are stained with anti-CD3 and anti-CD8 antibodies and counterstained with haematoxylin. The whole slides are then scanned to obtain digital images of the tissue sections and the densities of CD3+ and CD8+ T cells in the tumor centre and at the invasive margin are quantified using a dedicated Immunoscore® module integrated into a digital pathology software application.

Why Do CD3 and CD8 Antigens Matter Anyway?

The CD3 and CD8 antigens identify tumor-infiltrating lymphocytes (TILs). TILs are lymphocytes that have left the circulation to migrate to, and infiltrate, a tumor with the aim of eradicating tumor cells. However, as a result of the immunosuppressive microenvironment surrounding the tumor, TILs often end up as passive bystanders. Interestingly, the aim of some of the most successful immunotherapies that are being marketed today is to enable these passive bystanders and allow them to perform their anti-tumor functions. By targeting the checkpoint inhibitor proteins PD-1 and CTLA-4, drugs such as ipilimumab, pembrolizumab, and nivolumab disrupt the suppressive microenvironment in and around the tumor and re-active the TILs to attack and eliminate tumor cells.

Indeed, as evidenced by the award of the 2018 Nobel Prize, some exceptional results have been achieved for patients treated with anti-PD-1 and anti-CTLA-4 antibodies and the successes of immunotherapy have changed the way we think about cancer management. Nonetheless, while a blessing for responders, the reality is that a majority of patients will be disappointed in the results, if any, achieved with immune checkpoint inhibitor treatment. And so far, scientists and doctors have failed to understand why.

Taking the Immunoscore® to the Next Level

To date, the Immunoscore® has only been validated for colorectal cancer patients. However, scientist across the globe are working on modifying the Immunoscore® to allow prognostication in additional cancer types. Recently, a research group at Harbin Medical University Cancer Hospital developed an immunoscore assay, which incorporated PD-1 and PD-L1 expression, in addition to CD8. This assay was able to accurately predict disease-free survival and overall survival in patients with cervical cancer. A Norwegian research group adopted a similar strategy for non-small cell lung cancer, where a “PD Immunoscore” was developed based on the combined expression of PD-L1 on stromal immune cells and PD-1 on TILs. When adjusted for the additional potential markers CD8 and CD45RO, the PD Immunoscore accurately predicted disease-specific survival, disease-free survival, and overall survival in patients with non-small cell lung cancer.

Now, what if these findings could be taken one step further to not only predict survival, but to actually predict, prior to treatment, if a patient will respond to immunotherapy?

Identifying Immunotherapy Responders Using an Adapted Immunoscore

So far, the only research group to explore this possibility is a group from Guangzhou, China. They set out to develop an immunoscore model to predict the responses to anti-PD1 therapy in melanoma patients. Based on 134 pre-treatment biopsies, M0, M1, and M2 macrophages, CD8+ T cells, and CD4+ memory resting T cells were identified as the most prevalent immune cells in melanoma biopsies. Further parameter selection using LASSO logistic regression led to an intricate formula, which calculates an immunoscore based on a constant and each patient’s individual fractions of a large set of immune cells. In other words, a real-time mathematical model of the immune landscape in the tumor microenvironment.

A comparison of the obtained immunoscores against the patients’ response statuses showed that patients with a high immunoscore generally had better responses to anti-PD1 treatment than those who had a low immunoscore. Moreover, based on the patients’ pre-treatment immunoscores, the scientists were able to accurately predict their response to anti-PD1 treatment. The immunoscores also allowed identification of patients with an increased anti-tumor immune response and a higher frequency of TILs, further suggesting that the modified immunoscore can be used to picture the immune activity in melanoma patients.

As both physicians and well-informed patients become aware of the immunotherapy hype, the ability to easily identify potential responders becomes all the more important – both to fast-track responders and to avoid instilling false hope in patients who are unlikely to benefit from the treatment. While a modified immunoscore may not be the final answer, digital pathology and advanced multiplex immunohistochemistry methods will most certainly prove invaluable for deciphering the tumor microenvironment clues needed to identify immunotherapy responders.

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