# Monitoring response to immunotherapy using cell-free DNA fragmentomes

## Authors
Bahar Alipanahi, Lavanya Sivapalan, Jamie Medina, Zachary L. Skidmore, Paola Ghanem, Erica Peters, Gavin Pereira, Nisha Rao, Kavya Velliangiri, Alissa Konicki, Stephen Cristiano, Laurel Millberg, Jacob Carey, Keith Lumbard, Noushin Niknafs, Bryan Chesnick, Jennifer Tom, Alessandro Leal, Benjamin Levy, Patrick Forde, Peter Bach, Nicholas C. Dracopoli, Robert B. Scharpf, Victor Velculescu, Lorenzo Rinaldi, Valsamo Anagnostou

## Affiliations
Delfi Diagnostics, Inc., Baltimore, MD, USA; The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine; NYU Grossman School of Medicine

## BACKGROUND
- The rapid detection of disease progression in patients receiving immune checkpoint inhibitors (ICIs) is challenging given the lack of reliable biomarkers of clinical response.
- Current targeted next-generation sequencing cfDNA assays are costly and require a biomarker or prior knowledge about the mutations the tumor harbors.
- Here, we demonstrate the utility of DELFI Tumor Fraction (DELFI-TF), a tumor- and mutation-independent cfDNA fragmentome approach to monitor treatment response in patients with metastatic non-small cell lung cancer (mNSCLC).

## METHODS
- A cohort consisting of 47 longitudinal samples obtained from 15 mNSCLC patients undergoing treatment with both immunotherapy and chemotherapy was utilized to show the concordance with MAF and RECIST (Multi-Treatment cohort).
- Plasma-derived cfDNA was processed with whole genome sequencing (WGS) at low coverage (~4x).
- Circulating tumor burden was quantified as the fractional representation of ctDNA among total cfDNA.
- Matched white blood cells were used to filter out germline and clonal hematopoietic variants.
- DELFI-TF, a random forest regression model trained on MAF data from longitudinal blood samples of stage IV colorectal cancer patients (van 't Erve et. al. under review), was applied to predict the ctDNA fraction amongst samples.
- The accuracy of DELFI-TF was assessed using holdout validation in the ICI and Multi-Treatment cohorts.

## RESULTS
### TABLE 1. Participant and disease characteristics

| Characteristic              | ICI      |
|-----------------------------|----------|
| Participants(samples)       | 109(324) |
| Median Age                  | 68(36-91)|
| Sex,n(%)                   |          |
| Male                        | 59(54%)  |
| Female                      | 50(46%)  |
| Stage,n(%)                 |          |
| StageIII                   | 9(8%)    |
| StageIV                    | 100(92%) |
| Histology,n(%)             |          |
| Adenocarcinoma              | 84(77%)  |
| Squamous cell               | 23(21%)  |
| Large cell carcinoma         | 1(1%)    |
| Unknown histology           | 1(1%)    |
| Prior treatment,n(%)       |          |
| Yes                         | 72(66%)  |
| No                          | 37(34%)  |
| Treatment,n(%)             |          |
| Immunotherapy only         | 80(73%)  |
| Immunotherapy+ chemotherapy | 29(27%)  |

- DELFI-TF scores strongly correlate with maxMAF.
- DELFI-TF was applied to all longitudinal samples of the ICI and Multi-Treatment cohorts.
- Changes in DELFI-TF and maxMAF at all consecutive timepoints in the ICI cohort were highly correlated (n=215; r=0.9; Pearson).
- DELFI-TF dynamics are consistent with treatment outcomes.

Figures illustrate the correlation between DELFI-TF and maxMAF in the ICI cohort and Multi-Treatment cohort, showcasing strong correlations relevant to clinical outcomes.