Cancer treatment is entering an era in which the focus is shifting from attacking tumours with broad-spectrum therapies to understanding their biology, predicting how they will evolve and designing treatments tailored to individual patients. Artificial intelligence (AI), nanotechnology, gene editing and personalised immunotherapies are opening new possibilities, although major scientific and clinical challenges remain.
Explaining the future of cancer treatment, Dr Sameer Kaul, an eminent oncologist at Indraprastha Apollo, New Delhi, described cancer as a constantly evolving biological system that can adapt to therapies, evade the immune system, and develop resistance. He argued that this complexity demands equally dynamic and precise treatments.
“Cancer is not one big, giant monolithic enemy which needs to be defeated,” Kaul said, describing it “instead as a process of accelerated evolution occurring within the body”.
Why cancer is difficult to treat
“Cancer comprises more than 200 diseases, with substantial biological differences even among tumours affecting the same organ. As cancer cells multiply, genetic mutations create varied populations within a single tumour. Some cells may respond to treatment, while others survive and eventually drive a relapse,’’ Dr Kaul says.
He highlighted three mechanisms that help cancer survive: replicating indefinitely, evading immune surveillance, and developing its own blood supply.
“Cancer cells can keep dividing for a long time by protecting chromosome ends. They do this by activating an enzyme called telomerase, which helps them avoid the limits that normally stop cells from dividing,” he says.
Tumours can also trick the immune system. They use a pathway called PD-1/PD-L1 to weaken T cells, which are immune cells that can recognise and attack cancer cells. Meanwhile, tumours grow new blood vessels to get the oxygen and nutrients they need to grow. This process is called angiogenesis.
“Conventional chemotherapy has saved millions of lives, but its effects on healthy, rapidly dividing cells can cause significant side effects. Treatment can also select for resistant cancer cells, making subsequent disease harder to control,” he says.
Dr Kaul argued that the challenge is to anticipate cancer’s adaptations rather than continually responding after resistance develops.
AI could accelerate drug discovery
Artificial intelligence is emerging as a tool that could help researchers identify promising drug candidates, model biological interactions, and accelerate early-stage drug development.
Kaul distinguished earlier AI applications, which primarily recognised patterns in medical images and other datasets, from newer generative and agentic systems that can assist with more complex research tasks.
“These systems could help scientists identify molecules that block specific cancer-driving proteins while sparing healthy tissue, simulate molecular interactions, and refine candidate compounds before they enter laboratory testing,” he says.
He also referred to AlphaFold, the AI system developed by Google DeepMind that transformed protein-structure prediction. Understanding how proteins fold into three-dimensional shapes is important for identifying drug targets and designing molecules that interact with them.
“However, AI-generated candidates still require laboratory validation, preclinical testing, and clinical trials. Its potential lies in improving discovery efficiency, not eliminating the need to establish whether a treatment is safe and effective in humans,” he says.
Digital twins could predict treatment resistance
Another emerging concept is the use of digital twins — computerised models that represent a patient’s tumour and simulate how it might respond to different treatments.
“In principle, such models could help clinicians compare therapeutic options, anticipate resistance and identify alternative combinations before a patient’s cancer progresses,” he says.
Dr Kaul suggested that tumour digital twins could become an important part of personalised cancer treatment. But creating reliable models will require extensive patient-specific data, accurate representations of tumour biology and clinical evidence that predictions improve outcomes. Their routine use in cancer care is still a developing prospect, not an established standard.
Nanotechnology likely to improve drug delivery
A promising direction in cancer research is improving how drugs reach tumours while limiting exposure to healthy tissues.
Lipid nanoparticles, which rose to prominence through mRNA vaccine technology, can transport biological material into cells. Researchers are also investigating other nanoscale delivery systems to help therapeutic agents reach difficult targets, including tumours with protective microenvironments and cancers in the brain, where the blood-brain barrier limits the entry of many drugs.
Kaul described emerging approaches that use multiple biological signals to distinguish cancer cells from normal tissue. In such systems, a therapeutic agent might be activated only when it detects two conditions associated with a tumour, such as a particular cell-surface marker and an abnormal local environment.
This resembles a biological “logic gate”: the drug is designed to act only when the required signals coincide, perhaps improving precision and reducing damage to healthy cells.
Such strategies remain an active area of investigation, and their effectiveness depends on how reliably the chosen signals distinguish cancerous tissue from normal cells.
Cell therapies and personalised cancer vaccines
Cell-based immunotherapies are another important frontier. Chimeric antigen receptor (CAR) T-cell therapy modifies a patient’s T cells to recognise and attack cancer cells. It has transformed treatment for several blood cancers, although extending its benefits to solid tumours has proved more difficult.
Solid tumours can create an immunosuppressive microenvironment that prevents immune cells from functioning effectively. Researchers are examining ways to engineer cell therapies to surmount these barriers, including improving their persistence and ability to function inside tumours.
Kaul also discussed personalised cancer vaccines, including mRNA-based approaches designed to train the immune system to recognise tumour-specific targets. Unlike preventive vaccines against infections that can cause cancer, these therapeutic vaccines aim to stimulate an immune response against an existing malignancy.
Research into personalised vaccines has shown promise in cancers including melanoma, while studies are also exploring their potential in triple-negative breast cancer and other difficult-to-treat tumours. These approaches are still under investigation and are not universal cures.
Armoured (CAR) T cell therapies are in human trials so are the mRNA vaccines.
Earlier detection and targeted radiation
Advances in molecular imaging and radiopharmaceutical therapy could further change cancer management. These techniques use molecules that seek out specific targets on cancer cells, allowing clinicians to locate tumours and, in some cases, deliver radiation directly to them.
This approach can combine diagnostic imaging with targeted treatment, potentially improving disease detection and limiting radiation exposure to surrounding tissues.
Researchers are also investigating whether increasingly sensitive molecular tools could detect cancer earlier, including when the disease burden is low. However, detecting precancerous changes or tiny tumour deposits across the body still is a considerable scientific challenge.
Can cancer become a chronic disease?
Dr Kaul’s long-term vision is that advances in precision medicine will let more patients live longer, productive lives, even if cancer cannot be completely eliminated.
He compared this possibility with conditions such as hypertension, which can often be controlled through continuing treatment and monitoring.
The analogy captures an important aspiration, but cancer is not a single disease, and progress will vary considerably across tumour types, stages and patient populations. Some cancers are already highly curable, while others remain difficult to treat despite recent advances.
AI, personalised therapies and focused delivery systems offer reasons for optimism. Their ultimate impact, however, will depend on rigorous clinical evidence, affordability, and access—ensuring scientific progress translates into meaningful gains in survival and quality of life.