When I started teaching my graduate level course in Tumor Immunology & Cancer Immunotherapy in 2008, I did not use a textbook. Instead, I used seminal review articles and scientific research papers on novel and emerging therapies as the guiding content. The original Hallmarks of Cancer paper (circa 2000; published in Cell) by Doug Hanahan and Robert Weinberg, and later the Hallmarks of cancer: The next generation (circa 2011; published in Cell), served as the basis for my students understanding of the fundamental biology of the beast to be tackled. Years later, and no longer working in academia, I still use these principles as the guiding framework for my thinking as the ultimate “cancer fundamentals roadmap” with the latest installment of Hallmarks of cancer- Then and now, and beyond by Dr. Hanahan (Jan 2026; published in Cell).

https://www.cell.com/cell/fulltext/S0092-8674%2825%2901498-9

I pulled out a few of the newer insights below—and what they triggered for me as a scientist now working in drug development:

1. “Fifteen years later, there are clinically approved drugs targeting proliferative signaling, abrogated tumor suppression, apoptosis, angiogenesis, and immune evasion. However, for most patients, adaptive resistance and relapse eventually develop to such hallmark-targeting monotherapies.”—
   • To me, this represents substantial evidence of the impact of basic scientific research linked to approved treatments for cancer patients. Much of it originating from academic or NIH/NCI labs. #scienceinaction #applicationofbasicscience 
2. “Notably, co-targeting with distinctive drugs is a cornerstone of modern cancer therapeutics, often (but not exclusively) centered around the concept of hitting complementary vulnerabilities in cancer cells identified using ex vivo drug screening assays.” —
   • The cancer researcher and drug developers ode to  “Team work makes the dream work” as a foundational cornerstone of effective therapies. There is no magic bullet (effective single therapy) particularly when considering advanced and metastatic solid tumors. #cotargeting
3. “Next gen approach is attacking cancer ‘by land, by air, and by sea’. Three examples of the logic of hallmark co-targeting are presented by way of illustration” (the first two below have been tested clinically & approved) 
   • “Co-targeting angiogenesis w/ VEGF/VEGFR inhibitors + the immuno-evasive hallmark capability with immune checkpoint inhibitors (ICI) (e.g., anti-PD1/anti-PD-L1). Approved in renal, lung, hepatocellular, and endometrial cancers.” 
   • “Anti-VEGFA mAb (bevacizumab) + an inhibitor (olaparib) of the poly (ADP-ribose) polymerase (PARP) DNA repair enzyme-approved in ovarian. Similar approach approved in certain patients with acute myeloid leukemia, involves co-targeting resistance to cell death with the BCL-2 inhibitor (venetoclax) + epigenetic reprogramming (using azacytidine or decitabine).”
   • “The third example, which is intended to illustrate the logic of formulating new conceptual designs (ones that have yet to be clinically validated). One prominent mechanism for resistance to cancer immunotherapies involves a variety of cells in the TME that promote potent immunosuppressive activities. Therefore, a logical strategy is to block immune checkpoints on T cells + pharmacological reprogramming of CAFs, TAMs, TANs, cancer cells, and other accessory cells, to obviate their immunosuppressive capabilities.”—
     • Don’t threaten me w/ a good time! Rational combinations that could improve patient benefit is the work that I am personally the most passionate about. #rationalcombinations

Today, I am most excited to be a part of the collaborative efforts that can drive what will make it into this 3rd category in a future edition of this Hallmark series by “improving therapeutic benefits including their endurance” (by raising the bar for relapse and progression, which would require the induction of multi-faceted adaptive resistance mechanisms for each of the distinctive mechanisms being targeted).” —

• This is literally what gets me out of bed each day! I personally believe we are well on our way to achieving success here, and that the clinical & mechanistic DATA we’ve curated across the field to this point will pave that path. #datainsights #data integration

Using the DATA: A case for Modified Hallmark Maps

What could be really helpful, for those of us in the business of trying to discover & develop new and effective treatments, would be if the biology of highest relevance/activity could be differentially sized or visualized (e.g. bigger size segment relative to others), and separated by cancer type.  With the size of the segment being representative of how efficacious, or “active”, drugs targeting that pathway/hallmark have been historically, relative to targeting other hallmarks/pathways (I.e., relative observed Tx effect).

• For example, melanoma and lung (most sensitive to ICI) would have a bigger sized segment (area of the ring) for immune evasion, relative to less T cell-inflamed tumor types like MSS-CRC or prostate cancer.
• While something like EGFR-mut lung would have a larger sized segment for the cell proliferation hallmark.
• BRAF-mut melanoma would be up for debate between BRAF inhibitors targeting cell proliferation pathways (when looking at immediate tumor growth control) vs ICIs providing better long-term benefit (when looking at OS).  Even in this example, you can see that within a given tumor type there could be multiple drill-down visualizations across known segments, as well as by clinical associations (e.g., short-term tumor control (ORR or PFS) vs longer term OS benefit).

I suspect that vastly different biologic pictures would emerge if we compare across cancers, like say, classical Hodgkin lymphoma (paucity of tumor cells but w/ PDL1 gene amplification) vs melanoma (high TMB, TILs) vs gastric cancer (viral & bacterial associated inflammation). It sounds quite complex, but during the present time of AI evolution, it feels like AI/ML tools could easily help tackle this.

Could these “modified hallmark maps” better guide novel and effective co-targeting regimens?  

Maybe we need an AI collaborative grand challenge around this across industries!  

Abbreviations: CAFs (cancer associated fibroblasts), TAMs (tumor associated myeloid cells), TANs (tumor associated neutrophils), MSS-CRC (micro-satellite stable colorectal cancer), EGFR (epidermal growth factor receptor), BRAF (B-Raf protein gene; common oncogene in melanoma), ORR (overall response rate), PFS (progression free survival), OS (overall survival), TMB (tumor mutational burden), TILs (tumor infiltrating lymphocytes), AI/ML (artificial intelligence and machine learning)