A Human Tissue
Based Platform to
Advance Cancer Cell
Therapy Development

1Solid Tumors Present Challenges for Cancer
Cell Therapies

Addressing key translational model barriers
for cell therapy

Cancer cell therapy holds the promise to revolutionize theoptions for patients refractory to conventional treatments
including immune checkpoint inhibitors1.

Migration and chemotaxis throughheterogeneous solid tumor environments

While CAR-T and other cell therapies have shown remarkable
outcomes in hematologic malignancies, their efficacy in solid
tumors has proved to be more challenging2.

Persistence and exhaustion of cell therapy indiverse immune suppressive environments

Only 1 cell therapy has been approved for solid tumors and amere 14% of cell therapies advance from phase II trials, which
can be primarily attributed to high toxicity or lack of efficacy3.
A number of reasons underpin the lack of efficacy seen in theclinic. This is because solid tumors pose unique challenges for celltherapy persistence, migration, and chemotaxis4.

Figure 1: Cell therapy development process summary
Target ID and
Validation

Donor/cellsourceSelection

Cell prep

Representation of human tumor diversity

Ability to predict the in-situ patient response

CRITICAL GO/NO-GO JUNCTURE

Cell
engineering

Pre-clinicaltesting andcandidateprofiling

IND filing

INADEQUATE TOOLSFOR CELL THERAPY
TRANSLATIONALRESEARCH

Human tissue platform
This white paper presents the power of microfluidics andhuman tumor tissue alongside your cell therapy program:
Better Lead Optimization✓ Engineer candidates to elicit better chemotaxis andmigration through the human TME
Better Candidate Selection✓ Select for candidate properties that reduce exhaustionin human TME and under physiological stress
Better Candidate Profiling✓ Test candidates in diverse tumor tissues to establish
patient stratification

Support your analyses, results and conclusions with AIenabled, integrated analytics

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Box 1 abstract:
Cancer cell therapy has emerged as a promising next-generation modality for the treatment of solid tumors. However,moving assets from pre-clinical development to clinical implementation presents significant challenges, particularly inevaluating efficacy and understanding cell activity within complex tumor environments. This white paper explores thecurrent limitations of cell therapy translational research and how the Xsphera CT program can improve and de-risk preclinical to clinical stage programs by addressing the gaps and unmet needs, and the critical role of microfluidics inbridging this gap. By providing relevant in-vitro models that mimic the complexities of the tumor microenvironment,
microfluidic systems that bring together autologous, 3D human tumor architecture offer insights into cell therapy
efficacy, patient heterogeneity, and treatment mechanisms, ultimately facilitating precision medicine approaches incancer treatment.

The drug development cycle for cell therapy lacks
humanized models for candidate selection andprofiling
Safety, preliminary efficacy, immunogenicity andpatient stratification are key limitations totranslational success
90% of all drugs fail progress to approval, >30% fail going into phase
II and >50% fail in phase III5. While these numbers account for
multiple modalities, cell therapy is considered among them. These
failures are largely attributed to early pre-clinical decisions that affectsafety, toxicity and efficacy6.

Lack of translational models negatively impact
clinical success of cell therapy
Translating pre-clinical findings into clinical success remains asignificant bottleneck for cell therapy. Traditional biologicalmodels, including cell lines and animal models, often fail toaccurately recapitulate the complexities of human tumors,leading to discrepancies between pre-clinical efficacy and clinicaloutcomes.
Existing models often overlook critical aspects of the tumor
microenvironment, such as spatial organization, nutrient
gradients, and intercellular interactions, which play pivotal rolesin therapy response and resistance.

Why cell therapies for solid tumors fail in the clinic2,4,7-9

Traditionally, >50% oncology
failures happen in phase III

Manufacturing

Safety/Toxicology

Cell Therapy Fitness

Patient Diversity❑ Autologous CMC turnaround❑ Allogeneic
reproducibility❑ Donor and cell source
variability❑ Immune reactivity❑ Cytokine release
syndrome (CRS)❑ On-target off-tissue
toxicity❑ Auto-immunity❑ Prolonged exposure andexhaustion in TME❑ Lack of TME penetration❑ Lack of expansion in TME❑ Biomarker chemotaxis❑ Lack of persistence❑ Antigen presentation
variability❑ Inhibitory tumor
ligands (e.g.PD-L1)❑ Patient tumor diversity❑ Immune escape

Pre-clinical cell models that bring together microfluidics,
tumor heterogeneity, autologous tumor-immune
interactions and 3-dimensional tissue microenvironments
can predict these problems before entering the clinic.

Xsphera Biosciences addresses these widening
gaps in clinical translation of cell therapies.

3Pre-clinical models for cell therapy drug
development are emerging

Microfluidics combined with bioengineering are
the next generation biotools for cell therapy:

A number of advanced in-vitro models are emerging to enable thestudy of immuno-oncology drugs such as checkpoint inhibitors11,12.
2D in-vitro models remain the go-to platform for testing celltherapy affinity, potency and activity before developers advancecandidates to in-vivo models and ultimately patients. However,these tools limit the unique dynamics of cell therapy such as
migration through the extracellular matrix (ECM) and persistencein the tumor-immune microenvironment (TIME).

Xsphera CT and our microfluidic platform offers a promisingsolution to the challenges of pre-clinical development andclinical translation in cancer cell therapy. By leveraging
microscale technologies, these platforms enable the precise
control of fluid flow, cell behavior, and biochemical gradients
within three-dimensional (3D) tissue constructs, recapitulating
the complexities of the native TIME.

Migration through ECM:
Controlled experiments should
evaluate cell therapy target
tropism and chemotaxis incandidate selection stages.

Persistence in the TIME:
Key experiments in the lead
optimization stage should ensure
genetic modifications withstandthe pressures of the TIME.

Why microfluidics is advantageous➢ Plastics provide for real-time
imaging and analysis➢ Migration captured in 3D
across diverse patient TIME

➢ Longitudinal fluid sampling tocapture protein and cytokinechanges.

➢ Micronization of tumor
spheroids ensure biologicalreplication.

Comparing and contrasting biological models for the pre-clinical evaluation of cell therapy

HIGH

Probability of clinical failure

LOW

Patient stratification byclinical biomarkers
Biologicalcharacterization

Mitigation of technical risk

andIn-vivo(mouse)Efficacy

3D tumorimmune
co-culture

Chemotaxis

Primary
tumor
cells

Expansion in TME
Persistence in TME
Immunogenicity/tox
Early target affinityand potency

LOW

Microfluidic
ex-vivomodels2D cell
cultures

PBMC(healthy
donor)

Representation of patient diversity/heterogeneity

HIGH

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The Xsphera microfluidic system is a nextgeneration oncology platform

Robust internal and published data provide
confidence in the Xsphera Biosciences platform6-9

Unlike other microphysiological systems, Xsphera integrates a nextgeneration microfluidics platform with a proprietary clinical networkand cloud data infrastructure for high content data capture andbiologically validated analysis and interpretation.

The spheroid generation process is tightly controlled and yields
reproducible data and uniform biological components with complete
immune architecture13-17 (See Figure 1):❑ Accrual: Fresh human tumor tissue is sourced from qualified
clinical sites across North America, which include clinical data
and demographics.
❑ Processing: Tissue is micronized into spheroids using apatent protected, published protocol invented at Dana
Farber Cancer Institute.
❑ Devices: Spheroids are embedded in an extracellular matrix
and loaded into microfluidics devices with controlled gas
permeability and exchange.
❑ Actionable Endpoints: Cytotoxicity, transcriptomics, imaging
data, cell migration and phenotype, flow cytometry andproteomics are collected during and after each study.
❑ Data: Leveraging powerful visualization capabilities, AI andmachine learning, data are developed on a cloud interface
that integrates bioinformatics and analytics❑ Support: The Xsphera Biosciences team helps to analyze andinterpret data employing bioinformatics and 1:1 interactions.

DISCOVER
Identify better starting
material

SELECT
Select better candidates
that perform in humantumorsFigure 1: Xsphera microfluidic devices
Fresh patient
tumor samples

Processed into microfluidic
devices containing ECM

Intact, immunecompetent tumor
spheroids
Nucleus
Tumor CD45+

Within24hBrightfield

Patient stratification via mutations, pathology,
demographics information

COMBINE
Determine effective
drug combinations inthe same patient

STRATIFY
Stratify patient
populations before
entering clinical trial

Xsphera Bio microfluidics system

Figure 2: Correlation between in-vivoand ex-vivo response to
immunotherapy
CT26 tumor volumes for responder (R1+R2) andnon-responder (NR1+NR2) Balb/c mice were also
tested in the Xsphera microfluidics platform andlive/dead area was assessed at termination (Day6).

Jenkins RW et al. Ex Vivo Profiling of PD-1 Blockade Using Organotypic Tumor Spheroids. Cancer Discov. 2018 Feb;8(2):196-215. doi: 10.1158/2159-8290.CD-17-0833.

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XspheraCT is a purpose-built cell therapy program to improve candidate selection andprofiling by leveraging cell therapy specific activity in a patient derived microfluidic platform
with high content data generation and validated response criteria.

Xsphera CT brings together information on celltherapy activity with tumor and patient
heterogeneity

Peer-reviewed Xsphera Bio cell therapy data
supports platform confidence:

Migration of CAR-T, autologous and allogeneic cell therapies throughthe tissue ECM, into the tumor microenvironment and into cancer
cells is uniquely enabled by microfluidics (Figure 3). This feature ofcell therapy is a critical metric of activity and potency, which Xsphera
CT combines with cytotoxicity and cell killing, pharmacodynamics
and transcriptomics to elucidate patient heterogeneity.

Validated in peer-reviewed journals16, Xsphera CT can capture themigration of cell therapy using real-time imaging techniques,interconnect these evidence with anti-tumor effect, and accurately recapitulate the killing effect of clinically-developed umbilical cord blood
derived natural killer cells (aNK) (Figure 4).

Migration of Labeled CAR-TMedia compartment
Figure 3: Candidate selection enabled withactive chemotaxis in heterogeneoustumors:Microfluidic devices enable the active migration ofcell therapy through microscopic pores and tumor
ECM representative of the patient tumor
microenvironment, which can be studied in diverse
tumor samples with varying stromal content.

Tumor spheroid
compartment

Tumor killing

Infiltration into tumor spheroids

Fluorescently labeled allogeneic NK cells

Tumor
spheroids

100mm

Change in live tumor area (mm3)Target HIGH

Target LOW✱

Figure 4: Allogeneic NK celltherapy chemotaxis and killingassociates with tumor target
expression
Xsphera CT captures penetration of celltherapy, infiltration into tumor spheroids
and killing of tumor spheroids when thetarget on cancer cells is HIGH vs. LOW.

0-50000-100000-150000

Saha et al. Boosting Natural Killer Cell Therapies in GBM Using Supramolecular Cationic Inhibitors of Heat Shock Protein 90.
Front Mol Biosci. 2021 Dec 1;8:754443

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Goal: Adding a positive control alongside theexperimental therapeutic correlates the magnitude of
drug mechanism with response, and identifies patientsensitivity to pathway activation/down-regulation.

Tx A

Pos.
cont.

% change live
tumor area

Change in live tumor area post-treatment
with drug or positive control (IFNg + LPS)

Positive control experimental
designs associate mechanisms ofaction and antitumor activity

Patient samples

ID 1

CAR-T Tx

ID 3

Goal: Evaluating individual drugs across adiverse patient population provides unique,clinically-relevant and actionable information
on drug effect and patient heterogeneity.

CAR-T migration/
chemotaxis variability
across samples

ID 2

Drug efficacy and cell therapy
activity across patients

Change in live tumor area

Drug response varies across patientderived spheroids

Patient samples

Gene transcription post-treatment
provides critical information on MoA
and tumor killing in the same patient

Drug response to multiple agents (TX-A or TX-B) performed in asingle patient sample associates drug mechanism, efficacy andpatient diversity

Change in live tumor area

Live tumor area
CONTROL

TX-A

TX-B

Patient samples

Data analytics and interpretation

Patient stratification

Mechanisms of cell therapy
activity and action

Therapeutic efficacy❑ Baseline mutation status❑ Immunohistochemistry (IHC), H&E and pathology❑ Patient demographic and clinical history❑ Baseline tumor-immune flow cytometry❑ Spatial proteomics and transcriptomics profiling❑ Fluorescent microscopy and live cell tracking, migration and chemotaxis❑ Cytokine/chemokine protein expression❑ Post treatment gene transcription❑ Cytotoxicity❑ Live and dead tumor area
❑ Cell morphology
7Donor/ Cell
prep/engineering

Target ID and
Validation

Lead
optimization

Candidateselection

Candidateprofiling

IND filing

Representative case study:
Candidate Selection: To determine the activity and efficacy of two CAR
constructs in T-cell therapy within multiple patient samples

Goal

Desired outcome

To triage multiple cell therapy candidates expressing different CAR constructs forfurther selection and profiling towards IND.
Tumor type: Non-small cell lung cancer patient samples➢ Fluorescent imaging to detect migration towards target➢ CAR-T cytokine expression➢ Cytotoxicity and antitumor activity

Study design

Identify effective CAR engineered
therapies among multiple candidates
300+ tumor spheroids are assessed in every
experiment, which is performed multiple times acrossunique patient samples. At Candidate selction, CAR
can be compared for all endpoints. Analysis of tumor
spheroid live area is captured after 3-6 days of culture,
ex-vivo to assess effect on killing efficiency. Data areinterpreted by an algorithm that detects tumor killing
via high resolution fluorescent microscopy.
Data informatics is generated and delivered on thecloud based on a spheroid by-spheroid basis toconclude on population-wide efficacy and identify
more efficacious CAR-T.

Variability of CAR-T migrationand Spheroid Infiltration

Diverse efficacy with different CAR tested withinsingle patient samples and across multiple patients% Change in Mean Tumor Live Area

CAR-T

**
Live tumor
Dead tumor

CAR A

**

**

CAR B
001

002

003

004

005

NSCLC patient Sample ID

Patient-level interrogation to validate
mechanisms of action: Patient ID 004
Individual patient samples are analyzed for cell therapy activity
including migration towards the target, killing, transcriptomics andprotein cytokine changes after treatment.

Elevated CAR B Migration and activity
Control

CAR A

Mean live tumor area
across 300+ spheroids

Pharmacodynamic cytokine profile
elucidates MoA

CAR B

Live
Dead

CAR A

CAR B

ControlElevated CAR B Tumor killing
Control

**

CAR
A CAR
B

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How should my Xsphera CT
study be designed?

DECISION TREE

Have you identified your celltherapy target or clinicalbiomarker

We can provide primary patient samples, bulk RNA
seq, single cell RNA seq, spatial proteomics andtranscriptomics profiling to identify new biomarkers
and clinical targets for cell therapy.

NO

YES

Have you selected youroptimized donor material?

NO

Test article(s)Number of patient
samplesSingle celltherapyengineered fromdonor 1, 2, 3…

Minimum 5 patient
samples per donorderived cell therapy.
Recommend multiple
patient indications

Xsphera CT Experiment
Cell migration throughthe ECM
Tumor killing and posttreatment genetranscription, cytokine
expression

YES

Have you identified your leadcandidate and CAR construct forclinical development?

YES

Test article(s)NO

Test article(s)Number of patient
samples

Xsphera CT ExperimentMultiple celltherapycandidates
engineered withdifferent CAR orengineered
properties fromthe same donor
material

Recommended 10
patient samples per
candidate to ensure
target heterogeneity

Positive control design

Select a single tumor
indication

Cell migration through
ECM

Dose finding

Tumor killing and posttreatment genetranscription

Number of patient samples

Xsphera CT Experiment
Dose finding

Single cell therapy
candidate

Recommended 30 patient samples per indication toachieve likelihood of statistically significant
responses.
Multiple tumor indications selected from patientswith target pos./neg., suggested minimum of 3
indications

Baseline patient stratification (transcriptomics andmutation analysis)
Cell migration through ECM
Tumor killing and post-treatment genetranscription9References:
1. Ana Rosa Saez-Ibañez et al. Landscape of cancer cell therapies: trends and real-world data. Nature Reviews Drug
Discovery. 2022 Sep;21(9):631-632.
2. Grace Guzman CAR-T Therapies in Solid Tumors: Opportunities and Challenges. Curr Oncol Rep. 2023; 25(5): 479–
489.
3. 2023's Market Outlook For Cell And Gene Therapies. Cellandgene.com, February 2023.
4. Steven M. Albelda et al. CAR T cell therapy for patients with solid tumours: key lessons to learn and unlearn.
Nature Reviews Clinical Oncology | Volume 21 | January 2024 | 47–66
5. Richard K Harrison. Phase II and phase III failures: 2013-2015. Nature Reviews Drug Discovery volume 15, pages
817–818 (2016)
6. Andrew S. Khalil et al. Engineered tissues and strategies to overcome challenges in drug development, Advanced
Drug Delivery Reviews, Volume 158, 2020, Pages 116-139
7. Xiaoqing Li. Mechanisms of failure of chimeric antigen receptor T-cell therapy. Curr Opin Hematol. 2019 Nov;
26(6): 427–433
8. Yasmine St Martin Failure of CAR-T cell therapy in relapsed and refractory large cell lymphoma and multiple
myeloma: An urgent unmet need Blood Rev 2023 Jul:60:101095.
9. Perez et al. Failure of ALL recognition by CAR T cells: a review of CD 19-negative relapses after anti-CD 19 CAR-T
treatment in B-ALL. Front Immunol. 2023; 14: 1165870
10. Duxin Sun et al. Why 90% of clinical drug development fails and how to improve it? Acta Pharm Sin B. 2022 Jul;
12(7): 3049–3062.
11. Sara Abizanda-Campo et al. Microphysiological systems for solid tumor immunotherapy: opportunities andchallenges Microsystems engineering. 2023 Dec 15:9:154
12. Nicoletta Manduca et al. 3D cancer models: One step closer to in vitro human studies. Front Immunol. 2023; 14:1175503.
13. Sun et al. Targeting TBK1 to overcome resistance to cancer immunotherapy Nature. 2023 Mar;615(7950):158-167
14. Seghal et al. Dynamic single-cell RNA sequencing identifies immunotherapy persister cells following PD-1 blockade.
J Clin Invest. 2021 Jan 19;131(2):e135038,
15. Saha et al. Boosting Natural Killer Cell Therapies in GBM Using Supramolecular Cationic Inhibitors of Heat Shock
Protein 90. Front Mol Biosci. 2021 Dec 1;8:754443
16. Sade-feldman et al. Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma.
Cell. 2018 Nov 1;175(4):998-1013.e20.
17. Jenkins RW et al. Ex Vivo Profiling of PD-1 Blockade Using Organotypic Tumor Spheroids. Cancer Discov. 2018
Feb;8(2):196-215. doi: 10.1158/2159-8290.CD-17-0833.
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sales@xspherabio.com
www.xspherabio.com

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