Issue #58 // How to Kill a Tumor
Three perspectives on rationale drug selection in personalized cancer therapy
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Issue № 58 // Høw to Kill a Tumør
I was staring at a series of protein heat maps when I realized I had a problem. Not the usual SyntaxError problem. My code ran to completion and the visualizations it generated looked fine. The problem was philosophical. Depending on which analytical lens I applied to the same clinical trial dataset different proteins appeared to be the "best" therapeutic target. The first protein that caught my attention was EGFR T1068, showing a clean eight-fold up-regulation in tumor samples compared to adjacent healthy tissue, with pathway enrichment analysis showing the activation of downstream growth signaling cascades. Kaplan-Meier and Cox hazard analysis even showed EGFR T1068 to be a strong negative prognostic marker in the trial’s patient population. This looked like textbook oncogene addiction, where cancer cells had become dependent on hyperactive EGFR signaling for their survival and proliferation. The verdict seemed clear: inhibit EGFR, break the addiction, eliminate the cancer.
But, when I shifted my focus to surface proteomics data, searching for potential antibody-drug conjugate (ADC) targets, a different protein stood out. TROP2, also known as TACSTD2, showed high tumor-specific expression despite having no obvious role in driving malignant transformation in this specific context. Yet, it was abundantly present on tumor cell surfaces, and had excellent internalization kinetics, making it an ideal postal address to deliver cytotoxic payloads to. The logic for drugging TROP2 was fundamentally different from EGFR. TROP2 didn’t need to be a driver of cancer; it just had to be present, accessible, and able to carry a lethal package across the cell membrane.
Network topology analysis revealed yet another drug target candidate entirely. A protein called NEDD9 consistently showed up as a high degree and betweenness centrality hub in tumor co-expression networks while maintaining low centrality in normal tissue networks. NEDD9 wasn't dramatically over-expressed, showing only a modest ~1.3-fold elevation compared to normal samples. But its position at the intersection of multiple signaling pathways made it a critical coordinator of cellular communication within the malignant "ecosystem". Disrupt NEDD9, the network analysis suggested, and you could trigger cascading failures throughout the tumor's organizational structure—like removing a critical router from a communication network.
Three different analytical approaches; three different answers for how to kill a tumor. Each one backed by strong statistical evidence and reasonable biological rationale, and each one representing a distinct philosophy about what makes cancer vulnerable to therapeutic intervention. Are these competing truths—or just different projections of, and perspectives on—the same underlying biology?
The multiplicity of tumor killing philosophies didn't emerge in a vacuum; it reflects the evolution of cancer biology itself. The driver-focused approach stems from the work of Bert Vogelstein and others, in the 1980’s and 1990’s, who showed that cancer progression follows predictable patterns of genetic alteration. Their discovery, that specific oncogenes like RAS and tumor suppressors like p53 are mutated across many cancer types, provided the conceptual framework for targeting the molecular engines of malignancy. This was the era when cancer research began to feel less like taxonomy and more like engineering. If we could identify the "broken parts" in a cancer cell, we could fix them, or at least jam them up enough to stop the disease.
This mechanistic worldview dominated cancer research for decades, leading to some amazing successes. When Brian Druker pioneered the research and clinical development of Imatinib (developed by Novartis) to target the BCR-ABL fusion protein in chronic myeloid leukemia, he wasn’t just advocating for a drug—he was validating an entire philosophical approach to cancer treatment1. Here was proof that if you understood the molecular driver of cancer and could drug it effectively, you could stop cancer in its tracks. The results were dramatic enough that some oncologists started talking about "functional cures" for a disease that had been a death sentence just a few years earlier. Yet, even as targeted therapies revolutionized treatment for specific cancer subtypes, researchers were discovering the limitations of pure driver-focused approaches. Cancer cells, frustratingly, have the ability to develop resistance through secondary mutations and pathway rewiring. The very genetic instabilities that create driver genes and proteins also enable escape mechanisms that render them undruggable.
The use of antibody-drug conjugates (ADCs) emerged partly as a response to these limitations. If cancer cells can adapt and evade our mechanistic understanding, perhaps it warrants a shift from disrupting their biology to simply delivering cytotoxic payloads with high precision. The philosophy here is both cynical and pragmatic—stop trying to understand what makes a cancer cell tick, and just find a way to selectively poison it. This concept culminated in the development of compounds like Trastuzumab-Emtansine, combining the anti-HER2 antibody Trastuzumab with the cytotoxic agent Emtansine, where HER2’s role as a cancer driver becomes secondary to its utility as a giant bullseye on a tumor cell’s surface.
Meanwhile, the explosion of high-throughput sequencing in the early 2000s enabled systems biologists to construct comprehensive molecular interaction networks and identify emergent properties that weren't apparent from studying individual proteins in isolation. These researchers were asking fundamentally different questions. Instead of "which protein is dysfunctional?" or "which protein is abundant?", they asked "which protein, if removed, would cause the most widespread systems failure?" Network-based approaches promised to reveal organizational vulnerabilities that traditional reductionist methods might miss—nodes whose disruption could cascade through interconnected pathways to achieve therapeutic effects that exceeded the sum of their molecular parts2.
The tension between these different tumor killing approaches became increasingly apparent as I dug deeper into the oncology literature. EGFR T1068, the clear winner from my driver analysis, has been targeted clinically for years with mixed results. While EGFR inhibitors like Erlotinib have proven to be efficacious in treating lung cancer, their performance has been less stellar in other forms of cancer, like breast cancer. Unfortunately, this happened to be the experimental condition I was working on. So, despite having strong statistical evidence suggesting a certain therapy, the underlying experimental system had too many feedback loops, compensatory pathways, or microenvironmental factors that could potentially blunt it’s impact.
TROP2, my ADC target candidate, represented a different set of trade-offs. Recent clinical trials with TROP2-targeting ADCs had shown promising early signals, but the approach carried its own risks. Any surface protein abundant enough to serve as an effective delivery address might also be expressed at low levels in healthy tissues, creating potential toxicity concerns. The elegance of identifying tumor-specific surface markers has to contend with the messy reality of off-target effects and normal tissue cross-reactivity. The pharmaceutical literature was full of ADCs that appeared promising in preclinical models but caused dose-limiting toxicities in patients because the "tumor-specific" marker turned out to be expressed in the liver, lung, or other tissue at levels just high enough to matter.
This made NEDD9, with its high network centrality, conceptually appealing, but making the jump from network topology to a rational therapeutic target required navigating somewhat uncharted territory. After all, despite there being a number of proteins that have been shown to be network hubs across various cancer types, few have been successfully targeted in the clinic. The problems with network-based drug targeting is partly technical, partly conceptual. First off, high centrality-proteins are often structurally challenging to drug (large, flat surfaces). Additionally, while we’ve perfected the mathematical frameworks for identifying important nodes in a biological network, we’re still learning how to translate graph theory-based metrics into therapeutic strategies. For example, a protein may have a high degree and betweenness centrality on paper, while simultaneously being functionally redundant in practice, with parallel pathways compensating for its loss.
Yet, rather than viewing the aforementioned challenges as failures of individual targeting philosophies, I’ve begun to see them as complimentary perspectives on the same problem, or rather, orthogonal measurement of tumor vulnerability. A protein can simultaneously serve as a mechanistic driver, a payload delivery target, and a network hub, with each property lending itself to different therapeutic opportunities. Additionally, proteins that score highly across multiple analytical frameworks may represent particularly attractive drug targets precisely because they offer multiple routes to cell killing. For example, if EGFR monotherapy could be circumvented through pathway rewiring—with a notable example being the T790M mutation in the EGFR receptor that leads Gefitinib resistance—what about a protein that was simultaneously a driver, abundant on the surface, and a network bottleneck? Resistance would require the tumor to solve multiple problems at once.
With this in mind, I’ve been thinking about the idea of a multi-dimensional vulnerability assessment for potential drug targets. Instead of choosing between driver analysis, surface targeting, and network disruption, what if we scored proteins across all three dimensions simultaneously? For example, we can identify candidate protein targets based on their differential expression, pathway involvement, or network centrality, then, for each candidate in the list, we can generate a normalized score for each of these criteria (which accounts for different scaling), then create a composite score that combines the three individual scores (where each component is weighted). The weighting itself becomes a tunable parameter—you might weight surface expression more heavily if you’re specifically interested in ADC development, or prioritize network centrality if you’re looking for non-obvious targets that conventional approaches might miss. While most proteins show strong signals in only one analytical framework, a small subset emerge as multi-dimensional targets with high scores across multiple approaches3.
When I implemented this scoring system on my clinical trial dataset, something interesting happened. The top-ranked proteins weren’t necessarily the ones with the most dramatic signals in any single dimension. Instead, they were proteins that performed consistently well across all three frameworks—showing moderate over-expression, reasonable surface accessibility, and meaningful network positions. These were the proteins that traditional single-lens analyses might overlook in favor of more exceptional, but one-dimensional, candidates.
Consider the example of HER2 (aka, ERBB2) in breast cancer—a protein that initially gained attention as a driver oncoprotein but proved equally valuable as an ADC target and network hub. HER2 overexpression clearly drives tumor growth through activation of downstream signaling cascades, but its abundant surface expression also makes it a viable target for ADCs like T-DM1. Additionally, network analyses consistently identify HER2 as a high-centrality node that coordinates multiple aspects of tumor biology as it sits at the intersection of the PI3K/AKT/mTOR pathway, the MAPK cascade, and multiple other signaling networks that cancer cells depend on for survival and proliferation.
Thus, the clinical success of HER2-targeting agents may derive not just from inhibiting a key driver pathway, but from simultaneously disrupting a critical network node and providing a reliable delivery address for cytotoxic payloads. This multi-dimensional vulnerability may explain why HER2-positive breast cancers, despite their aggressive biology and fast growth, often show excellent responses to targeted therapy combinations. We’ve been attributing HER2’s success primarily to its role as a driver oncogene, but perhaps we’ve been underselling the contribution of its other properties. The fact that you can both inhibit HER2 signaling with small molecules and deliver toxic payloads via HER2-targeting antibodies gives oncologists multiple tools to attack the same target, making resistance that much harder to evolve.
Instead of asking "which protein is the best drug target?" the question now becomes, "which combinations of vulnerabilities can we most effectively exploit, while simultaneously minimizing toxicity?" If tumors present multiple, orthogonal vulnerabilities, then therapeutic strategies that address these vulnerabilities simultaneously might achieve synergistic effects while reducing the likelihood of developing resistance. This reframing also suggests why some clinical trials fail despite strong preclinical rationale. A protein might score perfectly on one dimension—say, as a clear driver—but if it lacks the other properties that make it druggable or deliverable, the therapeutic window might be too narrow for clinical success.
By recognizing that different analytical approaches reveal different but equally valid aspects of tumor biology, we can move beyond the philosophical debates that have sometimes divided the cancer research community. The question isn't whether we should target drivers, exploit highly abundant surface proteins with cytotoxic payloads that can be internalized by the cell, or disrupt networks—it's how we can most effectively integrate these approaches to achieve better patient outcomes.
If you liked this post, you may enjoy the following pieces from Sequence & Destroy’s Backlog:
I’d strongly recommend reading The Philadelphia Chromosome: A Genetic Mystery, a Lethal Cancer, and the Improbable Invention of a Lifesaving Treatment which tells the story Imatinib (Gleevec) — the first true targeted therapy for cancer.
I previously wrote about this concept in Leveraging Network Analysis For Drug Target Discovery.
What’s interesting about this approach, is that targets that show only modest differential expression, network centrality, and pathway involvement can still come out at top candidates when competing with proteins that may score highly in only one of these criteria.
Hello Evan, thanks for the article! I have been deeply thinking along similar lines for some time and have found many insightful ideas from your writings. Do you have any thoughts and opinions regarding usage of simulations for biological analysis? I would really like to know if you do!!
Thank you for the article, very insightful!
It's true that, when you look at different types of data, results are often different.
Many people talk about multi-omics approaches. However, in this case, I think that the three signals you have observed may have been diluted, and none of them observed.