Anti-FSP Antibodies May Predict ICI Outcomes in Lung Cancer


Findings from a study suggest that anti-frameshift peptide antibodies may also predict incidence of immune-related adverse effects in patients with lung cancer following immune checkpoint inhibitor therapy.

Use of a serological test using anti-frameshift peptide (FSP) antibodies may be predictive tumor response to treatment with immune checkpoint inhibitors (ICIs) in patients with lung cancer, according to findings from a study published in Journal of Translational Medicine.1

Anti-FSP Antibodies May Predict ICI Outcomes in Lung Cancer | Image Credit: © appledesign -

"The next challenge will be to predict which patients will require combination therapies for positive outcomes versus those [who] do not and to predict what type of IRAE they may experience," according to an expert from The University of Texas MD Anderson Cancer Center.

When investigators used a 226 peptide model to determine an aggregate contrast score for 66 lung cancer serum samples, outcomes were predicted in 69.7% of patients with an accuracy of 97.8%. The remaining 30.3% of samples were considered indeterminate because they were not bound to any model peptides. Two samples were collected from patients with EGFR-mutated non–small cell lung cancer (NSCLC) who were clinically observed and classified by the predictive model to be non-responders.

Excluding patients with stable disease, investigators identified 59 responder-specific FSPs and 207 non-responder-specific FSPs. A total of 173 FSPs overlapped with 226 peptides in the all-response model, which was 100% accurate in terms of predicting 78.7% of the samples.

Of 60 patient samples included in the immune-related adverse effect (IRAE) analysis, 18 patients had grade 2 to 4 IRAEs, and 42 had grade 0 or 1 IRAEs. Investigators identified 11 IRAE classifying peptides recurring in a minimum of 70 of 100 resampling iterations, which were all exclusively positive in a group of patients with symptomatic IRAEs. Overall, the results demonstrated that investigators could also analyze the same antibody binding profiles established on the FSP arrays for response predictions to develop a model predictive of IRAEs in lung cancer.

“In previous studies, other biomarkers have shown some encouraging results for predicting ICI therapy tumor responses,” senior study author Kathryn Sykes, vice president of Research and Product Development at Calviri, said in a press release on the study’s publication.2

“However, extraction and testing are elaborate, often unreliable, and sometimes not possible, and there is no test for predicting [AEs]. Our study explores anti-FSP antibodies as novel biomarkers, which can be simply and accurately measured from a small amount of blood.”

Sykes suggested that the potential utility of anti-FSP antibodies as biomarkers may extend beyond the lung cancer space.

“For other cancers, such as brain cancer—in which ICIs are not prescribed due to historically low response rates—a test to screen for patients who would respond could be life-saving,” Sykes said.

Investigators of the study collected serum samples from 74 patients with lung cancer and subsequently recorded tumor responses and IRAEs following administration of palliative PD-L1 therapy. Additionally, investigators assayed pretreatment samples on FSP microarrays representing 374,084 peptides that tumor cells can produce from translated mRNA processing errors.

Determining tumor radiologic responses involved use of RECIST v1.1 criteria, with “responders” defined as those with a radiologic complete response (CR) or a partial response (PR), and “non-progressors” as those with a CR, PR, or stable disease. Patients who received ICI treatment for at least 6 weeks prior to radiologic assessments had their serum samples included in the response analyses.

Of the patients with collected serum samples, 86% had NSCLC and 14% had small cell lung cancer. Following blood sample collection, 60% received ICI monotherapy, and 40% received combination regimens including chemotherapy. Investigators excluded 8 patient samples.

Overall, 1% of patients had a CR, and 34% had a PR following treatment with ICIs. Additionally, 18% and 36% of patients, respectively, had stable disease and progressive disease.

Investigators explored the effect of disease subtype heterogeneity by building a model including samples from 57 patients with NSCLC. The model included 281 peptides, which was deemed to be comparable to the full cohort model; it was 100% accurate with a sample classification coverage rate of 73.2%.

In a mapping analysis of the 226 informative peptides to the human reference genome, the informatically predicted RNA-error derived neoantigens of 3 genes were each the source of 2 classifying-FSPs. A gene ontology oncology analysis also found no enriched pathway in the 224 source genes corresponding to the mRNA misprocessing events capable of producing classifying FSPs in the full-cohort response model. However, some source genes in the FSPs used to develop the models predicting clear responses and monotherapy only were enriched in a few pathways.

The informatic analyses demonstrated that some FSP sequences that are ligands for antibodies associated with response outcomes correspond to variant transcripts of the same genes.

“Simple tests for accurate prediction of therapy outcomes would enable physicians to recommend treatments to patients most likely to respond, including those with cancers not usually responsive to ICI,” study author Mehmet Altan, MD, an assistant professor in the Department of Thoracic/Head and Neck Medical Oncology and Division of Cancer Medicine at The University of Texas MD Anderson Cancer Center, said in the press release. “The next challenge will be to predict which patients will require combination therapies for positive outcomes versus those [who] do not and to predict what type of IRAE they may experience.”


  1. Shen L, Brown JR, Johnston SA, Altan M, Sykes KF. Predicting response and toxicity to immune checkpoint inhibitors in lung cancer using antibodies to frameshift neoantigens. J Transl Med. 2023;21:338. doi:10.1186/s12967-023-04172-w
  2. Calviri scientists find new class of biomarkers for predicting treatment response in cancer patients. News release. Calviri, Inc. May 23, 2023. Accessed May 24, 2023.
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