# Integrating public single-cell transcriptomics and patient profiles to guide clinical development

<u>Jack Russella Pollard¹</u>, Joshua Whitener¹, Jordan Byck¹, Monika Manne¹, Hani Alostaz¹, Marianna Elia¹, Kamil Krukowski¹, Gabrielle Wong¹, Francisco Adrian¹, Liang Schweizer¹, Robert HI Andtbacka¹, &  Christos Hatzis¹  
<sup>1</sup>HiFiBiO Therapeutics, Cambridge, MA USA  
!

## BACKGROUND

Single-cell technologies provide invaluable insights into disease biology and inform drug development by revealing  
complex interactions among different cell types within patients. However, harnessing the potential of publicly  
available single-cell data remains challenging due to the lack of integrated data across diverse single cell platforms.

To maximize the potential of single cell insights, we have created an AI/ML powered curation and data integration  
process within our Drug Intelligence Science (DIS®) platform. This automated process identifies relevant published  
studies and integrates single cell transcriptomic data from publicly available sources with in-house generated datasets  
from our ex vivo translational efforts and our clinical programs.

## MATERIALS AND METHODS

### Dataset Search and Selection

Peer reviewed published datasets covering a variety of  
human tissues in the healthy and diseased settings (cancer  
and autoimmunity) are identified with a custom Python  
based dataset crawler that utilizes keywords to surface  
datasets and publications from various public repositories.

Dataset acquisition is aimed at improving indication selection  
and combination strategies for our clinical programs. For  
example, data from approximately 300 patients who received  
standard of care (SOC) treatment including immune  
checkpoint inhibitor (IO) therapy were selected for this  
purpose.

After filtering datasets for the availability of raw count data  
and vetting data quality via the recovery of the key findings  
from the publications, our current dataset composition is  
described in Figure 1.

**Figure 1. DATASET COMPOSITION.** ! Over 198 datasets were extracted from 186 publications (58 on  
autoimmune disorders including 866 patients across 30 indications such as Crohn’s and Sjogren’s !  
and 121 on cancer including 1476 patients across 39 indications such as lung adenocarcinoma and  
renal cell carcinoma).

### Metadata Extraction and Standardization

To structure the cell level data and render it suitable for  
integration and analysis, we extract metadata from each  
dataset/publication according to the guidelines for single cell  
data reporting (1). Briefly, attributes detailing these aspects  
are collected including:

- Sample processing-attributes (e.g., tissue dissociation, cell type  
enrichment, etc.)
- Data source (e.g., title, DOI, and number of patients), sample  
specific-attributes (e.g., indication, biopsy location, treatment, timepoint, response, etc.)
- Cell-level attributes (e.g., author annotated cell type/state, gene  
expression level, activation scores, clonotype, etc.)

These metadata are standardized where possible by  
referencing appropriate external ontologies such as the  
Disease Oncology (DO) for indications (2).

!**Figure 2. CURATION AND STANDARDIZATION WORKFLOW.**  Once datasets are identified, manual  
curation is used to extract both the raw data and the metadata in a standardized form. This  
standardized form is then used to process and integrate the datasets in a fully automated fashion.

### Generative Modeling for Single Cell Analysis Tasks

use of generative modeling for a variety of single cell tasks  
such as integration, normalization and cell type/state  
annotation. Typically, these tasks are done with best in  
breed-tools (3) that then require rank-based or meta-analysis  
to compare results across datasets. Generative modeling has  
the advantage of using a single, integrated model for all  
these tasks in an automated fashion and enables direct  
comparisons across datasets for machine learning  
applications.

For example, data integration and annotation harmonization  
of diverse cell types/states across studies is a computational  
challenge (4) that is ripe for generative approaches. Briefly,  
using the autoimmune and cancer datasets, we pre-trained a  
! model to annotate 31 immune and 4 non-immune cell  
types/states in new data sets in an automated fashion. In the  
latent space, new unlabeled data is integrated with data  
from the pre-trained model. Transfer learning then assigns  
the cell type/state annotation.

**Figure 3. PRETRAINED MODEL FOR CELL TYPE / STATE CALLING.**  By integrating more than  
377k cells drawn from 19 immune cell rich studies encompassing more than 411 samples  
from 225 cancer and autoimmune patients and healthy controls, we have created an  
algorithm to label cells from published and internal studies in an automated fashion.

## RESULTS

### Integrated Single Cell Database from Generative Modeling

### Number of Cells By Indication

**Representation of Cell Types in Diseases**  
!

! !**Figure 4. CELL COMPOSITION.** The current single-cell database comprises > 15 million cells analyzed in >5,000 samples collected from >2,500 patients  
across 160 curated unique human studies, spanning oncology (53%), autoimmune disease (AID, 24%), and viral infection (14%), with the remaining being  
from reference healthy tissues (9%). Only indications exceeding 0.09M cells are shown here.

The post-SOC resource was used to identify treatment settings where these mechanisms are enriched and markers co-expressed with targets of interest  
(e.g., PD1, CTLA4, OX40, BTLA, TNFR2, etc.) in specific cell populations to inform combination strategies.

### Cellect™: Multiuser Visualization and Interrogation Interface

**Figure 5. CELLECT.** To facilitate multiuser visualization and interrogation of the curated and integrated datasets, we have developed Cellect, a customized  
version of the CellxGene Gateway (5). The system has a suite of tools that help both bench and computational scientists find, explore, and analyze our  
curated single cell datasets. It includes several tools with features to engage with the data and aids in identifying cell types, clusters, and marker genes.  
Shown in the figure is an integrated dataset of about 1-million cells from cancer patients treated with checkpoint inhibitors across a variety of indications.  
Our generative modeling was able to standardize and integrate the data making comparisons across studies and treatments possible. This integrated  
dataset was used to identify combination strategies for our clinical programs.

### Post IO Combination Strategies Informed by Single Cell Data

## APPLICATIONS

**Figure 6. IDENTIFYING COMBINATIONS AND TARGET CELL TYPES.** To explore the cell type/states associated to IO response and resistance, a collection of  
about 475k cells from the primary tumor site of post IO treated patients was selected for analysis. Analysis across CD8 T-cells subsets that express several  
surface targets revealed a distinct pattern of expression with respect to the validated PD1 target (PDCD1). Specifically, the B- and T-lymphocyte attenuator  
(BTLA) and the Tumor necrosis factor (TNF) receptor 2 (TNFRSF1b) show patterns suggesting they maybe targets to activate distinct subsets of CD8 T-cells.  
!

### Post IO HFB200301 & HFB200603 May Target Distinct CD8 Subsets

### HFB200301 Hypothesis

### HFB200603 Hypothesis

**Figure 7. HFB200301 & HFB200603 MAY TARGET DISTINCT CD8 SUBSETS.** TNFR2 shows a pattern of expression consistent with it being an activating  
receptor expressed on an alternat ive set of CD8 T-cells not expressing PD1, that if agonized with a compound such as HFB200301 these cells maybe able to  
attack tumor cells. Alternatively, BTLA shows a pattern consistent with it being a coinhibitory receptor expressed with PD1 on CD8 cells in the post IO  
treatment setting, that if antagonized with HFB200603 in combination with anti-PD1 might activate those cells to attack tumor cells.

## CONCLUSIONS

We have presented an AI/ML guided approach to address the key challenge of integrating single-cell data across  
platforms and demonstrated that relevant disease biology is retained upon integration. We outline a path for  
deploying this solution at scale for bench and computational scientists to guide target as well as indication selection,  
as was done for our ongoing clinical programs, including our first-in-class TNFR2 agonist (HFB200301, NCT05238883)  
and our BTLA antagonist (HFB200603, NCT05238883).  
!

## References

1. Nature Biotechnology volume 38, pages 1384 1386.
2. Nucleic Acids Res. 2024 Jan 5; 52(D1): D1305––D1314.
3. Nat Rev Drug Discov. 2023 Jun;22(6):496-520.
4. Genome Biol 20, 194 (2019).
5. bioRxiv 2021.04.05; doi: https://doi.org/10.1101/2021.04.05.438318.