Omica · For Life Science

From inaccessible to research-ready.

Atla turns clinical data inside Latin American cancer centers into queryable research assets.

PLATFORM

Request patient data

Clinical records Structured EHR

Pathology Digital slides + reports

Molecular Genomic profiles

Outcomes Treatment response

Imaging DICOM + annotations

Data delivered

Modalities5 connected

Patients12,000+ longitudinal

StandardOMOP · FHIR · SDTM

Ready De-identified · Research-ready

Access oncology data from
underrepresented populations.

Longitudinal, de-identified patient data from oncology populations largely absent in existing research databases.

Data available

Clinical records

Pathology

Molecular profiles

Treatment outcomes

OMOP-CDM

Diverse populations

Genetic backgrounds, disease presentations, and treatment patterns absent from US and European datasets.

Longitudinal depth

Detailed treatment trajectories from diagnosis through follow-up, including lines of therapy and outcomes.

AI-enriched annotations

Pathology slides with model predictions linked to clinical outcomes. Designed for external validation or fine-tuning on underrepresented populations.

Can we enroll 500 TNBC patients with PD-L1 CPS ≥10?

ATLA Feasibility result 0.9 s

627 eligible patients identified across 4 institutions. Median enrollment projection: 14 weeks. All sites are IRB-compliant with structured PD-L1 data 1.

Feasible · 4 sites verified

1Network4 institutions · verified counts

Validate protocol feasibility
against real patient data.

Identify eligible patient populations across the network before committing to a site or a timeline.

Capabilities

Inclusion/exclusion queries

Verified counts by institution

Real-time availability

Protocol simulation

Test inclusion/exclusion criteria against real institutional data before finalizing your study design.

Site selection

Rank institutions by eligible patient volume, data quality, and regulatory readiness.

Population analytics

Subtype distribution, treatment lines, and demographic composition across the network.

Launch multi-site study · HER2+ observational

Sites activated 4 institutions

Patients enrolled 470 of 500 target

Study complete SDTM · OMOP export

Study Report · HER2+ Observational

Enrolled470 patients · 4 sites

Median PFS11.2 months (95% CI: 8.4-14.1)

DeliverablesSDTM · OMOP · PDF

Complete Submission-ready

Conduct clinical studies across
research-ready institutions.

Institutions with structured data pipelines, regulatory infrastructure, and pre-identified patient populations matching your protocol criteria.

Study types

Interventional trials

Observational studies

Real-world evidence

Post-market surveillance

Patient recruitment

Sites identify eligible candidates automatically against your protocol criteria, automating initial screening.

Model validation

Deploy and validate AI models against diverse populations before regulatory submission. Run inference inside the network, not outside it.

Regulatory readiness

Data mapped to SDTM, ADaM, and OMOP standards. Submission-ready from the start.

Purpose-built for oncology research in Latin America.

Integrated directly into institutional workflows.

INFRASTRUCTURE

Underrepresented populations absent from conventional datasets

Cancer patients across Latin America represent genetic backgrounds, disease presentations, and treatment patterns that US and European datasets do not capture.

Longitudinal depth with complete treatment trajectories

Detailed histories from diagnosis through follow-up, including lines of therapy, response, progression, and long-term outcomes.

Biospecimens connected to clinical context

Samples linked to the complete clinical history of the patient they came from. Available for validation, follow-up analysis, and biological grounding.

Local context, with global standards

Clinical data preserved in its original clinical context and mapped to OMOP, FHIR, SDTM, and ADaM for international research compatibility.

Deploy AI models against LATAM patient populations.

Run pathology, imaging, and clinical AI models inside the network. Validate against populations absent from your training data, or deploy licensed models into institutional workflows.

Your data, your terms. Institutional governance throughout.

Every participating institution chooses which studies to join and which partners to engage. Access is permissioned, queries are logged, and consent is explicit.

The AI Platform for Oncology
Research in Latin America.

From inaccessible to research-ready.