Feb 4, 2022 / Oncology

Predicting therapeutic response in lung cancer using a novel immune cellular biomarker

Olivia Habern

To raise awareness for World Cancer Day, we profile research conducted by scientists from the Icahn School of Medicine at Mount Sinai, NY that may provide a new predictive biomarker for immune checkpoint therapeutic response in non-small cell lung cancer (NSCLC). These findings could help better inform therapeutic decisions by more precisely stratifying patients according to the immune composition of their tumors, working in coordination with other tumor features, such as tumor mutational burden.

Reading the biomarkers of immunotherapy response

Though we’re in an era of incredible advancement in cancer immunotherapy, we still face challenges in reliably predicting the right treatment for a patient’s specific tumor. Only 20–40% of cancer patients will actually respond to immunotherapy (1). Furthermore, the tradeoff of potentially dangerous side effects as a result of co-opting the body’s immune system makes it crucial for clinical oncologists to identify the best patient candidates for immunotherapy. 

There are a few different readouts that oncologists employ in order to make this treatment decision. Single biomarker testing can be used to assess the expression levels of PD-L1 on the surface of cancer cells, informing the likelihood of clinical benefit from anti-PD-1/PD-L1 drugs (1). Another biomarker of immunotherapy efficacy is referred to as the tumor mutational burden (TMB)—a readout of the number of mutations in cancer cells (1). When a high TMB is observed, it is hypothesized that these cancer cells are more likely to produce proteins that a patient’s immune system would recognize as foreign, making the cancer more vulnerable to an immune response boosted by immune checkpoint inhibitor (ICI) therapies. However, TMB is still up for debate as a stand-alone biomarker. It can vary across cancer types, necessitating consistent, disease-specific standards to accurately quantify whether a tumor’s TMB is high or low (1). 

Additionally, many clinical studies are increasingly showing the value of considering multiple biomarkers in determining response to immunotherapy: 

“[Results] from a phase II trial with atezolizumab, a therapeutic anti-PD-L1 antibody, in patients with metastatic urothelial cancer, indicated that biomarkers such as PD-L1 expression and TMB provide independent and complementary information about the tumours’ response to the treatment. The emerging picture is that a combination of markers is going to be required to predict a patient’s response.” (1)

These insights point to a need for the discovery of new tumor biomarkers that, in conjunction with TMB or PD-L1, can improve tumor classification and inform the crucial decision-making process for immunotherapy treatment. 

Defining immune populations in non-small cell lung cancer

One relatively unexplored relationship in tumor biology is that between the immune composition of the tumor and subsequent response to immunotherapy. Though T-cell infiltration is an emerging hallmark of a generally positive response, it is unknown how the tumor immune microenvironment prior to treatment dictates this response and what specific immune cell populations or cell states may be predictors of response to ICI therapy. Additionally, as oncologists look to build a case for immunotherapy, there is a need to understand the link between these immune features and tumor mutational burden. Are they redundant readouts of the immunotherapy response, or do they act as complementary predictive biomarkers? 

Led by Dr. Miriam Merad, and first author Dr. Andrew Leader, a team of scientists from Icahn School of Medicine at Mount Sinai, NY recently undertook a study to answer these questions in the context of non-small cell lung cancer (NSCLC), a class of carcinomas that arise from the cells lining the surface of the lung airways (2). To investigate the composition and molecular states of immune cells in NSCLC, the team turned to single cell RNA-sequencing (scRNA-seq), with combined surface protein profiling and immune repertoire assessment through CITE-seq and T-cell receptor sequencing (TCR-seq), respectively. They performed scRNA-seq on 27 untreated, matched patient samples, including cancer tissue and non-involved lung tissue. They also performed multiomic profiling through CITE-seq on 8 patient-matched samples, followed by combined scRNA-seq and TCR-seq on 3 patient-matched samples. Analyzing 361,929 single cells from 35 tumors, this represents the largest single cell map of the early-stage lung cancer immune response to date (3). 

Lineage-normalized single cell data across all tumors revealed 49 immune clusters among 6 major classes of cells: T cells, B cells, plasma cells, mast cells, plasmacytoid dendritic cells, and mononuclear phagocytes. Combined scRNA-seq and surface protein data revealed a diverse mix of T cells, resolving unique populations and activity states within CD4+ and CD8+ T-cell clusters. One cluster, noted as Tactivated, was enriched in IFNG, GZMB, LAG3, CXCL13, and HAVCR2 transcripts and increased PD-1, ICOS, and CD39 protein expression, some of which have direct relevance to the underlying mechanisms of ICI therapies. This group of Tactivated cells, along with regulatory T cells (Treg), was the most prevalent among a cluster of T cells the researchers called Tcycle—a mixture of multiple T-cell phenotypes that shared the cycling state, characterized by expression of cell-cycle genes MKI67 and STMN1, and surface proteins HLA-DR and CD38.

A new cellular signature of tumor-immune activation

With this detailed view of the immune composition of the untreated tumor microenvironment, the research team noted correlations of cell-type frequencies between certain immune populations across tumors. High correlation was seen among Tactivated cells, IgG+ plasma cells, and other monocyte populations, which collectively formed the lung cancer activation module (LCAMhi). Anticorrelation was seen between B cells, TCentral Memory (CM)/naive-like-II, and dendritic cells, among others, which composed an LCAMlo cellular signature, and resembled the immune composition seen in non-involved lung tissues. Importantly, patients were well stratified according to an average of the cell types represented in these modules, suggesting the diverging cellular signatures could represent a new readout of patient diversity. 

To explore the broader potential significance of the LCAM score, the research team rescored bulk transcriptomic data from 512 lung adenocarcinoma tumors documented in The Cancer Genome Atlas using gene signatures representative of and specific to the cell types within the LCAMhi and LCAMlo modules. These scores correlated with the published data of total immune content. Retrospective analysis of bulk data also showed that the recorded tumor mutational burden was also correlated with the LCAM score for respective lung tumors, as was a score quantifying the expression of mutated cancer neoantigens. These promising correlations suggested that the LCAM cellular module reflects an adaptive immune response to mutated tumor antigens that is independent of total immune infiltration (3). 

Clinical data further solidified the potential significance of the LCAM score as an indicator of immunotherapy response. Analyzing the results of a 2016 clinical trial of NSCLC patients who were treated with either atezolizumab (anti-PD-L1) or chemotherapy, the team found that both LCAM score and TMB were correlated with improved progression-free survival in patients who were treated with immunotherapy. This evidence for survival benefit, taken together with the quality of the active immune cells that make up the lung cancer activation module, may suggest LCAMhi patients experienced a more robust antigen-specific antitumor adaptive immune response (3). This also positions LCAM as a viable complementary predictive biomarker of immunotherapy response to checkpoint blockade. 

Improving immunotherapy predictions with multiomic single cell analyses

This study represents an important step forward in the ongoing effort to improve immunotherapy approaches for cancer patients. With a single cell multiomic view of the cellular composition of  the tumor microenvironment, including the ability to distinguish and refine immune cell states, researchers were able to identify a specific cellular and molecular signature associated with immune activation that could be further tested and confirmed across larger tumor cohorts. Equipped with the LCAM biomarker, and the tools to bring to light other undiscovered predictive biomarkers, there is hope that we can begin to understand all of the multidimensional tumor-immune features that dictate immunotherapy response. 

Learn more about this study here →  


  1. Decoding the signs of response to cancer immunotherapy. Nature Research Custom Media and Illumina.
  3. Leader AM, et al. Single-cell analysis of human non-small cell lung cancer lesions refines tumor classification and patient stratification. Cancer Cell 39: 1594–1609.e12 (2021). doi: 10.1016/j.ccell.2021.10.009