Trends in Biotech for Oncology

🧬 Oncolytic Viruses: Engineering viruses to kill cancer cells — and nothing else. What if you could program a virus to selectively infect, replicate in, and lyse tumor cells... and only tumor cells? That’s the idea behind oncolytic virotherapy, a growing area in cancer immunotherapy that uses modified viruses to target and destroy malignant cells — while also stimulating the immune system. This diagram breaks down the modular approach scientists take to designing oncolytic viruses: 1️⃣ Choose a viral backbone: Adenovirus (Ad5), lentivirus, or AAV — each with different genome sizes and delivery potential. 2️⃣ Engineer the capsid: Add tumor-targeting elements like metalloprotease-activated fibers or polymeric shields to boost selectivity. 3️⃣ Control transcription: Use tumor-specific promoters to restrict gene expression to cancer cells. 4️⃣ Add shRNA to knock down host genes (e.g., immune suppression, replication support). 5️⃣ Use post-transcriptional targeting: Employ miRNA response elements or codon optimization for even more precision. 6️⃣ Insert suicide genes (like thymidine kinase): These activate prodrugs, turning the virus-infected tumor cell into its own executioner. 💥 Once the virus replicates, tumor cells burst — releasing viral particles + tumor antigens, helping prime the immune system to recognize cancer. With clinical trials underway and approvals like T-VEC for melanoma, this is a field worth watching closely. Have you seen oncolytic viruses combined with checkpoint inhibitors or CAR-T therapy in your research or studies? #OncolyticViruses #CancerImmunotherapy #Virotherapy #SyntheticBiology #TumorTargeting #MedicalInnovation #Biotech #MolecularMedicine #CancerResearch *Made with BioRender*

🔬 𝗕𝗶𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗔𝗻𝘁𝗶𝗯𝗼𝗱𝗶𝗲𝘀 𝗶𝗻 𝗦𝗼𝗹𝗶𝗱 𝗧𝘂𝗺𝗼𝗿 𝗢𝗻𝗰𝗼𝗹𝗼𝗴𝘆: 𝗔 𝗦𝗵𝗶𝗳𝘁𝗶𝗻𝗴 𝗧𝗵𝗲𝗿𝗮𝗽𝗲𝘂𝘁𝗶𝗰 𝗣𝗮𝗿𝗮𝗱𝗶𝗴𝗺 The clinical landscape for bispecific antibodies (BsAbs) in solid tumors is expanding rapidly, with 𝟲𝟴𝟭 𝗮𝗰𝘁𝗶𝘃𝗲 𝘁𝗿𝗶𝗮𝗹𝘀 and 𝟭𝟴𝟯 𝘂𝗻𝗶𝗾𝘂𝗲 𝗕𝘀𝗔𝗯𝘀 under investigation as of 2025. Driven primarily by 𝗯𝗶𝗼𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 rather than large pharmaceutical firms, this field is witnessing a pivot toward 𝗶𝗺𝗺𝘂𝗻𝗲 𝗰𝗵𝗲𝗰𝗸𝗽𝗼𝗶𝗻𝘁 𝗰𝗼𝗺𝗯𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀, with a strong emphasis on PD-1/CTLA-4, PD-1/VEGF, and EGFR/c-MET targets. 📈 From 2019 to 2024, the number of BsAb trials has more than doubled, reflecting a sharp increase in investment and scientific focus. However, 𝗼𝗻𝗹𝘆 𝟯𝟴% 𝗼𝗳 𝘁𝗿𝗶𝗮𝗹𝘀 𝗶𝗻𝗰𝗹𝘂𝗱𝗲 𝗯𝗶𝗼𝗺𝗮𝗿𝗸𝗲𝗿-𝗯𝗮𝘀𝗲𝗱 𝗶𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻, indicating substantial room for improvement in precision approaches. 🧬 𝗔𝗺𝗼𝗻𝗴 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗰𝗼𝗺𝗺𝗼𝗻 𝗯𝗶𝗼𝗺𝗮𝗿𝗸𝗲𝗿𝘀:    • EGFR (31%)    • HER2 (29.8%)    • PD-L1 (21.3%) These biomarkers dominate a space where precision targeting is still underutilized. Notably, Cadonilimab, a dual PD-1/CTLA-4 inhibitor, leads development with 175 trials, including promising outcomes in gastric and cervical cancers. 🌍 The global distribution of trials is concentrated in the 𝗨𝗦 (𝟰𝟳𝟴 𝘁𝗿𝗶𝗮𝗹𝘀) and 𝗖𝗵𝗶𝗻𝗮 (𝟰𝟳𝟮 𝘁𝗿𝗶𝗮𝗹𝘀), followed by the 𝗘𝗨. Emerging biotech sponsors like Akesobio are reshaping the innovation pipeline, underscoring the field's dynamic shift toward 𝗯𝗶𝗼𝘁𝗲𝗰𝗵-𝗹𝗲𝗱 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗺𝗼𝗱𝗲𝗹𝘀. ⚠️ Challenges remain: manufacturing complexity, off-target toxicity, and limited biomarker integration. But next-generation BsAbs—targeting novel combinations such as PD-L1×TGF-β or CD47×PD-L1—signal potential breakthroughs in 𝗶𝗺𝗺𝘂𝗻𝗲-𝗿𝗲𝘀𝗶𝘀𝘁𝗮𝗻𝘁 𝘁𝘂𝗺𝗼𝗿𝘀 and 𝗰𝗼𝗹𝗱 𝘁𝘂𝗺𝗼𝗿 𝗺𝗶𝗰𝗿𝗼𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀. 🎯 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲-𝗔𝘄𝗮𝘆𝘀: • 681 clinical trials are exploring BsAbs in solid tumors—doubling since 2019 • PD-1/CTLA-4 is the most studied immune checkpoint pair (216 trials) • Biomarker-driven trial design is lacking (only 38%) • EGFR, HER2, and PD-L1 dominate as biomarkers; precision targeting is still emerging • Cadonilimab shows improved OS and PFS in multiple cancers • Biotech firms are leading innovation, with Akesobio spearheading 178 trials • Novel targets (e.g., PD-L1×TGF-β) aim to overcome immunotherapy resistance #OncologyInnovation #BispecificAntibodies #ImmunoOncology #PrecisionMedicine #Biotech #ClinicalTrials #SolidTumors #CheckpointInhibitors #CancerTherapeutics #Biomarkers Rafik ElBeblawy, Chinmay Jani, Judith Pérez Granado, Mark Gramling & Aakash Desai, MD, MPH, FASCO

Every year, more than 2 million people worldwide are diagnosed with breast cancer, 1.1 million with gastric cancer, and ~600k with bladder cancer - and across all three, HER2 remains one of the most aggressive molecular drivers. Against this backdrop, HER2-targeted oncology is entering one of its fastest periods of innovation in a decade. The field is expanding beyond traditional monoclonal antibodies into ADCs, bispecifics, TKIs, and IO-based combinations, with momentum building simultaneously in breast, gastric/GEJ, and urothelial cancers. Across tumor types, 2025 delivered multiple late-stage readouts that point to a consistent theme: therapies are moving earlier in the treatment pathway. Later-line efficacy signals are now translating into first-line strategies with meaningful gains in PFS and, in several cases, clear OS improvements. In metastatic HER2-positive breast cancer, the shift is already visible. Daiichi Sankyo and AstraZeneca’s Enhertu + pertuzumab regimen delivered one of the strongest first-line PFS readouts in the field (DESTINY-Breast09): median PFS of 40.7 months vs 26.9 months and a 44% reduction in the risk of progression. Pfizer’s HER2CLIMB-05 reinforced the same trajectory, with tucatinib added to first-line maintenance (trastuzumab + pertuzumab) meaningfully lowering progression risk. A similar acceleration is taking place in gastric and GEJ tumors, historically a challenging setting after trastuzumab failure. The HERIZON-GEA-01 trial demonstrated that zanidatamab, with or without tislelizumab, can deliver statistically significant improvements in PFS and clinically meaningful trends in OS versus trastuzumab + chemotherapy, positioning it as a potential new first-line standard. Meanwhile, the later-line landscape continues to strengthen with Enhertu and Anbenitamab (KN026), both showing compelling activity in previously treated disease. In urothelial cancer, HER2-directed innovation is gaining ground as well. Disitamab vedotin + toripalimab delivered strongly positive Phase 3 data in RC48-C016, improving both PFS and OS regardless of cisplatin eligibility or HER2 expression level. The results highlight the potential of ADC + IO combinations to redefine first-line strategy in HER2-expressing urothelial carcinoma. Across solid tumors, the takeaway is increasingly clear: HER2-targeted therapies are consolidating their place earlier in the disease course. The pace of innovation suggests the next five years may look very different from the last decade, with HER2 evolving into one of the most strategically important molecular franchises in oncology.

Spatial Omics Comes of Age: From Pretty Maps to Predictive Oncology Spatial omics is no longer just about adding coordinates to single-cell data. This comprehensive review makes a compelling case that spatial technologies, paired with multimodal analytics and AI, are becoming a core translational engine for oncology. The real inflection point is not higher plex or finer resolution per se, but the ability to distill spatial complexity into scalable, clinically actionable biomarkers that matter for patients and drug development. Key Topics: ◾ Technology maturation across modalities: The field has rapidly diversified beyond spatial transcriptomics to include spatial proteomics, metabolomics, and same-section multi-omics. Commercial platforms now span subcellular to tissue-scale resolution, FFPE compatibility, and near–whole-transcriptome coverage, lowering the barrier to clinical adoption. ◾ Analytical breakthroughs unlock biology: Emerging computational frameworks - graph models, multimodal integration, and foundation models - are converting spatial patterns into mechanistic insight. Spatial niches, cellular communities, and ligand–receptor signaling are now being resolved in situ, not inferred post hoc. ◾ From 2D snapshots to 3D ecosystems: Serial-section reconstruction and volumetric imaging are redefining how we study tumor evolution, immune exclusion, and therapy resistance. Tumors are increasingly understood as 3D, spatially organized ecosystems rather than flat cellular atlases. Potential for Drug Development ▪️ Target discovery: Spatial context reveals where pathways are active, not just if they are, critical for prioritizing druggable interactions and avoiding false positives from dissociated systems. ▪️ Biomarker strategy: High-plex discovery assays can be distilled into lower-dimensional, AI-enabled diagnostics suitable for trials and eventual clinical deployment. ▪️ Patient stratification: Spatially defined immune niches (e.g., TLSs, perivascular hubs, stem-like T cell reservoirs) are emerging as predictors of response to immunotherapy and combination regimens. ▪️ Translational realism: In situ profiling reduces dissociation bias and captures fragile or rare cell states that often drive resistance and relapse. Standardization, scalability, and cost remain limiting factors. The next wave of innovation will favor platforms and analytics that translate spatial discoveries into robust, reproducible assays compatible with clinical workflows and regulatory expectations. Spatial omics is transitioning from a discovery luxury to a translational necessity. The winners will be those who can bridge exquisite spatial biology with pragmatic clinical readouts - keeping patients, not pixels, at the center. Read the full article: https://lnkd.in/eNvUdCPX #SpatialOmics #PrecisionOncology #DrugDevelopment #TranslationalScience #CancerResearch

💡 Next-Gen Cell Therapy: Can Microfluidics Solve ACT’s Biggest Challenges? 🔬 The promise of adoptive cell therapy (ACT) in oncology is undeniable, yet challenges in scalability, cost, and consistency limit its broader clinical impact. Could microfluidic technology be the breakthrough we’ve been waiting for? A recent Nature Biomedical Engineering review highlights how microfluidics is reshaping ACT manufacturing, offering precision, efficiency, and affordability across the entire workflow; from cell isolation and gene editing to expansion, functional selection, and potency assessment. 🔹 How Microfluidics is Transforming ACT 🔬 Scalable & High-Purity Cell Isolation ◾ Microfluidic sorting (FACS/MACS) enables high-speed, high-purity enrichment of tumor-reactive immune cells. ◾ Magnetic microfluidic separation (MATIC) isolates potent CD39+/CD103+ TILs from blood, bypassing the need for tumor resection. 🧬 Next-Gen Gene Editing—Beyond Viral Vectors Non-viral gene editing (mechanoporation, electroporation) reduces mutagenesis risks and cuts manufacturing costs by up to 45%. 🦠 Smarter Cell Expansion & Bioreactors ◾ Microfluidic bioreactors boost cell densities by 100x, reducing footprint and turnaround times. ◾ Enabling on-site ACT manufacturing for faster patient access. 🎯 Functional Selection of High-Potency Cells ◾ Nanovials capture single-cell cytokine secretion, allowing selection of highly cytotoxic T cells. ◾ Shear-stress assays identify strongest TCR clones based on real tumor-cell binding strength. 💡 Predicting Efficacy & Toxicity with Microphysiological Systems (MPS) ◾ 3D tumor models in MPS simulate immune responses, improving ACT potency assessment before infusion. ◾ Reducing risks of cytokine release syndrome & on-target/off-tumor toxicity. 🚀 The Future of ACT Manufacturing Microfluidics is ushering in a new era of decentralized, cost-effective, and highly potent cell therapies. With automation, AI, and advanced biomaterials, we’re moving toward a faster, safer, and more accessible future for cancer treatment. 🔗 📖 For an in-depth review of these advancements, please refer to the full article here: https://lnkd.in/d2aRpwDm #CellTherapy #AdoptiveCellTherapy #Microfluidics #TCellTherapy #GeneEditing #Biomanufacturing #Oncology #CancerTherapy

Navigating the landscape of modern cancer treatment can feel like driving through a storm without headlights 😵💫 If traditional chemotherapy is like driving a car with a gas leak - spraying toxins everywhere in hopes of hitting the destination - Antibody-Drug Conjugates (ADCs) are the GPS revolution An ADC isn't just a drug; it’s a high-tech delivery vehicle 🚘 💡 The monoclonal antibody is the "GPS" that targets specific proteins on cancer cells 💡 The payload is the "Package" (chemotherapy) to be delivered once the GPS reaches its destination 💡 The linker is the "security lock" that keeps the package attached until it's inside the cell This clever mechanism allows us to bypass healthy cells and deliver the hit straight to the tumor But even with a map, you can’t avoid every pothole 🕳️ ADCs may be more targeted, but that doesn’t mean they don’t have toxicities - they do have a job to do after all When you understand the mechanism of the payload in ADCs, the side effects start becoming predictable clinical patterns rather than something to memorize For example: 👉 Enfortumab vedotin’s payload is a microtubule inhibitor so you should think of neuropathy 😬 👉 Sacituzumab govitecan’s payload is SN-38 (the same active metabolite of irinotecan) so you should think of diarrhea 🚽 ADCs aren't just a trend - they are becoming the backbone of oncology care with a robust pipeline of new entities being studied If you try to memorize every new drug name as it launches, you'll always be playing catch-up. But when you build a solid baseline foundation in mechanisms and payloads, you won't just be managing today's patients - you’ll be ready for the next decade of oncology breakthroughs before the first vial even hits your pharmacy. 👇 Which ADC "pothole" has been the most challenging to manage in your practice lately? --- I’m the Kelley in KelleyCPharmD 👋 and I help pharmacists learn the complex world of oncology 📧 Want my help? Join 3000+ other pharmacists learning from my Oncology Insights Newsletter or DM me to learn about the ELO Collaborative, the only oncology pharmacist training program built with expert support, community, and real-world application at the center

In the U.S., a powerful flywheel is redefining how precision oncology moves forward: 1️⃣ Assays: Labs leverage comprehensive and/or multiomic platforms to perform routine biomarker testing on patient samples. 2️⃣ Databases: These data flow into multiomic databases, paired with patient-level clinical and outcomes data. 3️⃣ Partners: Labs, academic groups, and pharma/biotech partners mine these datasets to identify novel pathways, biomarkers, and signatures. 4️⃣ Algorithms: Algorithms with clinical or commercial value are validated on the assay platform and integrated back into the offering — driving utilization. 5️⃣ Acceleration: Higher assay utilization spins the flywheel faster. Several companies — such as the “Big 5” shown here (Caris Life Sciences, Tempus AI, Foundation Medicine, Guardant Health, Natera) — are already building versions of this model. Diagnostic companies that can’t (or don’t) replicate key components will struggle as biomarkers evolve beyond molecules and toward algorithms. Key strategic questions as the flywheels spin: 💡 To what extent are payors willing to cover broad multiomic testing when much of the data is “non-actionable”? 💡 Do revenue opportunities from other parts of the flywheel offset potentially suboptimal reimbursement? 💡 What’s the role — and risk — for distributed assay kit manufacturers? 💡 What does precision medicine innovation look like outside the U.S., where this model doesn’t yet exist? 💡 How should labs prioritize investment across the different parts of the flywheel? 💡 How will data ownership, access rights, and IP be managed across assay developers, data integrators, and pharma partners as these flywheels become increasingly interconnected? Bottom line: The race is on to master the full precision-oncology flywheel — from assays and databases to algorithms and partnerships. Those who can synchronize all four nodes will shape the next decade of precision medicine.

Want to know where Pharma is placing early portfolio bets? Look at their preclinical pipelines. That offers early signal on emerging targets, modality shifts and competitors resource concentration. But aggregating that data (and keeping it up to date in real-time) at scale is hard - millions of data points scattered across patents, abstracts, presentations and company websites. That's where Sleuth comes in. I used Sleuth to aggregate >500 preclinical oncology programs across 12 Pharma leaders and surface insights - here were 2 patterns that stood out: 1. Amongst these oncology powerhouses, half of all PC programs are unique ~50% pursue solo targets (~25%) or target combinations (~25%) that no other company in this group is pursuing. It's simultaneously true that Biotech is driving more industry R&D (>2/3 of recent FDA approvals came from biotech sponsors) and there's a long tail of independent Pharma exploration. Even where multiple programs cluster (4+ in this peer set), most targets have limited late-stage development industry-wide, including MCL-1, SMARCA2, TEAD, MSLN and GPC3 2. Emerging modalities show extreme concentration, but most players hedge their bets Across all 500+ programs, the split is what you'd expect: traditional small molecules dominate, with everything else from bispecifics, cell therapy, ADCs, radiopharma and degraders all clustered together. Within each company, the strategic concentration can be more striking: • ~75% of radiopharma programs are concentrated in just 3 companies (AZ, BMS, Novartis) • ~75% of cell therapy programs are in 4 (AZ, Amgen, Roche, AbbVie) • Merck alone has 40% of all preclinical trispecific oncology programs, while Roche / Genentech dominate in BsAbs But here's the nuance: most companies hedge strategically. Only 2 of 12 lack any cell therapy or radiopharma and only 1 lacks ADCs. Companies maintain broad coverage while concentrating resources where they see advantage - balancing optionality with deliberate bets. S&E and BD teams we work with tell us preclinical data like this changes how they prioritize gaps and time partner discussions. If that resonates - reach out. Always happy to share data and discuss what insights would move the needle for your team. As always, comment below to get a hi-res PDF.

Liquid biopsies are transforming cancer care, and adoption is accelerating! Here are some interesting takeaways from Rishikesh’s latest analysis leveraging our NGS DxBook: 1. While therapy selection (vs. MRD / monitoring) is the most mature clinical application for LBx, adoption remains limited: only ~1/3 of surveyed labs currently integrate LBx into their workflows. There is untapped potential as growth barriers (see below) are addressed. 2. Institutional adoption varies widely. Academic Medical Centers (AMCs) and national reference labs are at the forefront of LBx adoption, with BioPharma showing significant momentum. In contrast, community hospitals lag behind resulting in an opportunity for targeted outreach and education to expand access. 3. Barriers to adoption persist. Inconsistent reimbursement policies, a lack of definitive clinical utility data, and a lack of E2E workflows continue to hinder broader adoption. Yet, the transformative potential of LBx is undeniable: - Comprehensive genomic profiling (CGP) can double actionable genomic alteration detection rates compared to smaller panels, enhancing biomarker-matched therapy decisions. - Emerging applications, like assessing gene functionality in real-time, promise to elevate precision therapy for next-gen treatments, including antibody-drug conjugates (ADCs). 4. While Europe lags the U.S. in LBx adoption, progressive labs are rapidly catching up, as reflected in our dataset. Feel free to share if this maps to the trends you are observing! Or ask Rishi in person next week at #AMP2024! #NGSisUnstoppable