Authors: Advik Chandok and Aiza Dada
Mentor: Katherine Ferris. Katherine is a doctoral candidate in the Department of Oncology at the University of Oxford.
Abstract
Cancer grows more prevalent by the year, from 1.9 million new cases in the US in 2022 to over 2 million in 2023; despite this increase in cases, it is not met with a proportional increase in treatment progress, due not only to a lack of funding, but also a lack of success. This challenge can be attributed, in part, to the fractured nature of this research. Due to the wide array of individual treatments being tested for efficacy in clinical settings, it is difficult to see the progress that might be achieved with a more coordinated approach. One reason for this division is the emergence of personalized medicine, which has stirred much excitement for the future of cancer treatment by mitigating the challenge of tumor heterogeneity in current treatment. However, exorbitant costs coupled with the need for more research have posed a barrier to its wider implementation. Thus, it is important to note that the one-size-fits-all approach to treatment has its own merits, and to not lose sight of these in the quest for a breakthrough in individualized treatments. Just as personalized medicine overcomes the limitation of heterogeneity, a more generalized treatment overcomes the practical limitations of the individual approach, suggesting that a combined method holds the most promise of success.
Introduction
Throughout history, many diseases have been bested. First was smallpox when Dr. Edward Jenner manufactured a vaccine with cowpox in 1798, eventually leading to its eradication in 1980: the first and only disease eradicated. Tuberculosis was cured with four drug treatments; polio with an easily accessible vaccine. Today, much attention has been brought to defeating cancer. So far, efforts have seen limited success. In 2022 there were 1.9 million new cases, the following year 2 million. Most of these new cases are arising in a younger population as young adults are the only age group with an increase in cancer incidence (Katella, 2024).
Cancer is characterized by the unregulated division and growth of abnormal cells in the body. These cells can invade and destroy normal body tissue. Unlike normal cells, which grow, divide, and die in a regulated manner, cancer cells continue to grow and divide uncontrollably, forming masses in the body called tumors (“Cancer - Symptoms and causes”, 2022). While not all tumors are cancerous (benign tumors do not spread to other parts of the body), malignant tumors invade nearby tissues and can metastasize to distant organs, posing significant health risks.
The hallmark of cancer is its ability to bypass the regulatory mechanisms that control cell growth. This uninhibited growth results from genetic mutations that activate oncogenes, deactivate tumor suppressor genes, or affect genes involved in DNA repair. These mutations allow cancer cells to proliferate indefinitely. Additionally, cancer cells can evade apoptosis (programmed cell death), which normally serves as a critical mechanism to eliminate damaged or unneeded cells (“What Is Cancer? - NCI”, 2021).
Metastasis is another defining characteristic of cancer, wherein cancer cells spread from the primary tumor to distant sites in the body (“What Is Cancer? | Cancer Basics”, 2024). This process involves a series of steps: local invasion, intravasation into blood or lymphatic vessels, survival in the circulatory system, extravasation into new tissue, and formation of secondary tumors. Metastasis is responsible for the majority of cancer-related deaths, as metastatic tumors can disrupt the function of vital organs.
Cancer is not a single disease but a collection of various diseases. Tumors exhibit substantial heterogeneity, both within a single tumor and between tumors in different patients (Marusyk et al. 2012). This heterogeneity arises from genetic mutations, epigenetic alterations, and environmental influences. It complicates treatment because different cells within a tumor may respond differently to therapies, leading to resistance and recurrence, and because different patients may respond differently to the same treatment (Dagogo-Jack et al., 2018). Understanding and addressing this heterogeneity is crucial for developing effective cancer treatments.
Cancer is not a novel disease. Cancer has been found as early as 1.7 million years ago in a human relative who most likely sustained a tumor in their left toe bone. Ancient surgeons that knew of this disease also knew that it could and most often did come back even after surgical removal (Nalewicki, 2023).
In 1846, surgeons Bilroth, Handley, and Halsted removed tumors alongside entire lymph nodes to minimize risk of resurgence (at great risk to patients). More than 40 years later, Paget reported on the spread of cancer to other places in the body, which today is known as metastasis. Soon, understanding the mechanisms of cancer was also highlighting the limitations of surgical removal.
While many improvements in surgery have been made such as better imaging technology (PET scans, CT scans, MRI scans, and ultrasounds), camera-assisted surgery, and even cryosurgery (freezing cancer cells to kill off tumors), other methods have accompanied surgery as the first line of treatment.
Chemotherapy, radiotherapy, immunotherapy, and other adjuvant treatments have been developed to counter cancer on the macro scale, intended as the first treatment for most patients today. These treatments have been highly successful in reducing cancer mortality and have advanced progress substantially.
Recent years have once again given us a new treatment approach entirely: targeted cancer therapy. These personalized treatments are not meant for large-scale use; rather, they treat tumors by targeting qualities of cancer unique to small subsets of patients. Such treatments include growth signal inhibitors such as imatinib, endogenous angioinhibitors like endostatin which prevent tumors from forming blood supplies, and other precision care treatments in immunology such as CAR-T therapy which weaponizes a patient’s own T-cells to fight their cancer (Sudhakar, 2009).
Today, researchers argue the importance of one approach over the other, creating a divide in literature. Treatments are no longer improving as fast as they once did; progress was five times faster in the 2000s than in the 2010s. With ever decreasing research funds, it is of utmost importance to maximize the efficiency of future research (Kennedy, 2024). Thus, this paper aims to assess the efficacy and merits of both approaches to determine the best path forward for future research.
The One-Size-Fits-All Approach
Definitions and Theory
A one-size-fits-all treatment for cancer has long been sought after, both in the media and in clinical settings. Having a single treatment applicable to any kind of cancer would eliminate the need to screen patients and identify populations, increasing the ease of distribution of medication. The theory behind it is that by identifying either a common genetic alteration or phenotypic trait among cancers, we could design a treatment that targets that universal commonality to be used in all patients.
For the purposes of this paper, a one-size-fits-all treatment will be defined as a singular treatment modality, meaning that the steps of treatment are the same amongst all patient populations and require no screening to identify sub-populations. Thus, treatments that may differ biochemically amongst individual patients would still be classified as one treatment as long as they preclude the need to modify our treatment steps in any way.
These treatments must also be able to differentiate between tumor tissue and healthy cells. While cancer cells originate from once healthy cells, there are key differences that medications can exploit. These differences include the rapid reproduction of cancer cells, a lack of growth suppressing proteins, and genomic instability to name a few (see Figure 1).
Figure 1: Hallmarks of cancer cells. It’s important to note that there are multiple mechanisms cancer can use to express any of these traits.
Radiotherapy is critical to oncological care today: it is involved in 40% of cured cancer cases. By delivering high doses of energy-packed photons/particles (x-rays, gamma rays, electron beams) to damage the DNA within cells. While this hurts our normal cells, by targeting cancer’s hallmarks it affects it far more. First, it slows down the rapid proliferation of cancer since the damage to their DNA will either prevent or prove to be fatal at cell division. Second, radiotherapy exploits the genomic instability of cancer; while healthy cells will be able to repair the damage to their DNA, cancer cells lack the same repair mechanisms due to their genomic instability. Combined, these two factors mean that radiation will be far more detrimental to tumors than healthy cells (Baskar et al. 2012).
There are also treatments that target down-regulated tumor suppressor genes like mutant p53, the most common oncogenic protein in cancer, present in more than half of all human cancers (Ozaki, 2011). There are multiple treatment modalities for mutant p53. Some have used p53 as a drug target, selectively killing cancer cells. One such example is MDM2 inhibitors, a major negative regulator of p53, preventing it from entering the nucleus and binding with DNA. They demonstrated a worthwhile clinical response, but considering recorded adverse events, combining with adjuvant therapies such as chemotherapy or immunotherapy may prove to be more effective. Other treatments instead exploit principles of synthetic lethality: in genes with linked functions, when a mutation rendering one of those genes inactive wouldn’t impact the cell significantly, a concurrent mutation in the other gene would completely shut out this cellular function, thus proving lethal for the cell. In cancer cells with mutant p53, p53 can no longer activate survival-promoting pathways through G1 when cells undergo stress; however, mutant p53 promotes compensatory pathways in S and G2 arrest to accomplish the same function (see figure 3a by Hu et al., 2021). By inhibiting these compensatory pathways, it increases genetic stress resulting in mitotic catastrophe (Hu et al., 2021). And still other treatments focus on fixing mutated p53 genes such as adenovirus-assisted gene therapy which boasts solid safety profiles and proven to function both as a monotherapy and also boost the efficacy of chemo and radiotherapy (Roth, 2005). This research paper will focus on assessing the efficacy of these treatment options and the applicability of a one-size-fits-all treatment for cancer in the first place.
Advantages
A one-size-fits all approach to cancer treatment holds its fair share of benefits. Standardized treatments simplify the development of treatment protocols, allowing healthcare providers to train staff more easily while also enabling them to implement treatment far more efficiently. They would also require far less screening since a larger percentage of the patient population would qualify for these treatments; this enables faster and more timely treatment. These large-group treatments could also be mass produced, lowering the cost of treatment thereby increasing affordability and availability for patients regardless of socioeconomic/geographic background. Not only would a standardized treatment have benefits for the patient, but it would also improve research quality. It would allow researchers to concentrate resources on a smaller group of treatments, potentially accelerating the discovery and optimization of therapies. Not only that, but these group treatments would have more clinical data backing each one of them up, again allowing these treatments to improve at a faster rate.
Timely and Efficient Treatment
Time is a matter of great importance when it comes to cancer treatment modalities; being able to treat a patient days earlier can be significant when determining the efficacy of the treatment. Thus, having options that can be implemented as a sort of “first-line” of treatment is crucial in treating any disease, not just cancer.
For cancer, surgery can be and is one of those first-line treatments. It’s a conceptually simple treatment which involves removing the tumor locally and can be utilized as soon as the tumor is identified in a patient. Looking at adults with brainstem high-grade gliomas, a cancer which we still do not know much about, surgery offered these patients many months. Whereas patients who only underwent a biopsy survived a median of eight months post-diagnosis, a partial resection added 3 months to that, and a gross total resection saw patients live a median 16 months post-diagnosis (Doyle et al. 2019).
Chemotherapy and radiotherapy are also crucial, considered by many oncologists as the gold standard for cancer treatment. Both chemo and radiotherapy operate on the basis that cancer cells divide far more frequently than their healthy counterparts, and by delivering toxic and radioactive elements that impair cellular function and reproduction, cancer cells would be most impacted. Indeed, concurrent chemo and radiotherapy improved five year survival 10% in cervical cancer patients (Chino et al., 2020).
Lower Costs and Improved Affordability
Cost is also a big factor in treatment; even if effective, if a treatment isn’t affordable for the majority of the patient population, there is little use for it. However, costs remain exorbitant for many cancer treatments today. The American Cancer Society Cancer Action Network reports that 73% of cancer patients were worried about payment, and 51% were in debt (“Survivor Views: Cancer & Medical Debt”, 2022).
Large-scale treatments can be a solution to the cost dilemma we are faced with. Brucella Melitensis, a bacteria that can manipulate the microenvironment of the human body and assist T-cell therapy in killing/providing immunity. By manipulating the microenvironment of tumors, resistance to T-cell therapy was artificially decreased and tumor cell growth almost completely halted; host survival was 100%. This treatment is expected to cost $1 per dose (Guo et al., 2022).
Research Efficacy
Research for cancer is extremely time consuming and expensive, but researching into one-size-fits-all treatment modalities improves the efficacy of research since improvements in one-size-fits-all treatments benefit the majority of the patient population. The same amount of research impacts more patients and improves more lives.
Clinical research for oncology in phases 1-3 costs a combined $37.8 million per study, and including a phase 4 trial, they amount to $78.6 million (Sertkaya et al., 2014). Broader treatment options like radiotherapy can treat a large portion of cancer patients; according to AdvaMed, representing all major global radiation therapy manufacturers, 50-60% of patients receive it (“Radiation Therapy”, 2024). Of the 2 million new US cancer cases in 2023, that would amount to 1-1.2 million patients receiving that treatment. Compare that to a specialized drug like imatinib, which is primarily used to treat chronic myeloid leukemia (CML), specifically BCR-ABL translocation cases, present in 95% of CML patients. In those specific cases, it can bind close to the ATP binding site, blocking it and inhibiting the enzyme activity of that protein, halting downstream signaling of tumor development (Iqbal et al., 2014). Overall, the patient population imatinib can treat is fairly small; only 8,816 patients in the US (95% of the 9,280 cases predicted for 2024) can receive it (Katella, 2024). Assuming that both one-size-fits-all and individualized treatment research would cost the same, the cost of research per patient treated is far cheaper with broad treatments: $65.50 per patient compared to $8,915.61 per patient for individualized treatment. Broad treatments are 13,600% (136x) more cost effective.
Research for one-size-fits-all treatments would also be easier to execute once treatments were used in the clinical setting due to the fact that there would be a larger database of research. With more data points, it becomes increasingly easy to identify harms with treatments, and then improve them. This is seen most obviously in chemotherapy which has evolved throughout the year. Today, new adjuvant treatments can easily be developed for it as well since the side-effects and interactions of chemotherapy have been well documented.
Limitations
While such treatments pose several promising advances in the cure for cancer, it is important to note their limitations and downsides. Although cancer cells can be characterized by certain universal traits, such as a large nucleus, irregular size/shape, and prominent nucleoli, each cancer has different causes, even within the same cancer type. Not every cancer cell in a tumor will appear or behave in the same way as a neighboring cancer cell, meaning certain cells may be able to survive or evade a particular treatment. Each broad cancer type has numerous subtypes which individually express themselves in a different manner due to their unique genetic and molecular makeup. As such, while one treatment may work to address a particular cancer subtype or phenotypic expression, it is quite challenging to discover a treatment that is feasible over 100 types of cancer and their various subtypes.
Problems with Resistance One great limitation in the search for a treatment to eliminate all forms of cancer is the development of drug resistance by cancer cells. This resistance is particularly prevalent in one-size-fits-all traditional cancer therapies and can manifest itself in many ways. Conventional treatments apply selective pressure to cancer cells, killing those that are sensitive to the drug while allowing others to thrive and proliferate. Over time, these resistant cells become the dominant population, leading to treatment failure. Due to the diversity and various genetic mutations of tumor cells, a one-size-fits-all treatment may not be able to effectively target every cell. Heterogeneity in resistance mechanisms makes it challenging to discover a second, more efficient global treatment. Furthermore, due to the high mutation rate of cancer cells and consequential likelihood of mutations that confer resistance to cancer drugs, the specific cause of resistance becomes difficult to pinpoint with the implementation of a broader treatment (“Why Do Cancer Treatments Stop Working? Overcoming Treatment Resistance”, 2016). Some cancer drugs, including methotrexate (see Figure 3 from Arbel et al., 2018), aim to inhibit specific enzymes in key pathways that facilitate cell growth and division (“Cancer Drug Resistance”, 2024).
However, increased transcription of the gene that controls levels of the target molecule can cause a significant increase in the amount of that target molecule in the cell. Since the concentration of the drug is limited by the dosages that can be given, the rapidly increasing numbers of target molecules result in many targets that remain unaffected by the drug. Due to the effects of gene amplification, there are simply too many targets for the number of drug molecules present to efficiently attack. Additionally, the upregulation of growth receptor ligands in response to inhibitory drugs is an influential factor in the development of drug resistance in cancer cells. When cancer cells are exposed to inhibitory drugs, they can activate compensatory survival pathways. One common mechanism is the upregulation of growth receptor ligands, which bind to their respective receptors on the surface of cancer cells, activating signaling pathways that promote cell survival, proliferation, and resistance to the drug. Upregulated ligands can activate parallel or downstream pathways, allowing cancer cells to bypass the blockade imposed by the drug; in other words, when one pathway is inhibited, another may become activated to compensate. For instance, in non-small cell lung cancer (NSCLC) treated with EGFR (epidermal growth factor receptor) inhibitors, resistance often develops through upregulation of the MET (mesenchymal epithelial transition) receptor and its ligand, HGF (hepatocyte growth factor) (Qin et al., 2023). This bypasses the inhibited EGFR pathway, prompting the growth of cancer cells despite treatment.
Moreover, due to certain gene changes in cancer cells, patients with the same type of cancer may still respond differently to the same drug. For example, in chronic myeloid leukemia, the tyrosine kinase inhibitor (TKI) called imatinib targets the BCR/ABL oncogene, which exacerbates genomic instability and cancerous cell growth. However, point mutations in the BCR/ABL oncogene can cause resistance to imatinib, specifically in the Thr315 and Phe317 residues by directly inhibiting imatinib-binding affinity (Valent, 2007). As such, the binding capacity of the protein to the oncogene is reduced, and the medication is no longer capable of efficiently managing and suppressing malignant cell transformation.
Distinguishing between Healthy and Tumorous Tissue
Another limitation to consider with large-scale cancer therapies is the mechanism of delivery and its efficacy across various forms of cancer. These treatments tend to rely on properties, such as rapid cell growth, that are shared with healthy tissue but more pronounced in cancer cells, making it a method that is not entirely reliable and foolproof for separating the two groups. Delivering cancer treatments effectively to tumor sites while minimizing impact on healthy surrounding tissue is a difficult task influenced by several factors. These include the physical and biological barriers posed by the tumor and its microenvironment, the properties of the therapeutic agents, and the body’s own defense mechanisms. While adoptive cell therapy has shown promise in using T-cells to target cancer cells as a form of immunotherapy, the complexity of genetically engineering macrophages, as well as their susceptibility to the tumor’s immunosuppressive microenvironment, makes it a uniquely difficult approach despite its potential benefits. Scaling up the production of therapeutic cells to treat large numbers of patients also remains a significant challenge. Photodynamic therapy (PDT), which uses a drug activated by light to attack cancer cells, may be a less invasive and targeted approach to treating several types of cancer (“Photodynamic Therapy to Treat Cancer - NCI, 2021”). When activated by light of a specific wavelength, photosensitizing agents, which preferentially accumulate in cancer cells, produce reactive oxygen species that kill the cancer cells. However, its use on a larger scale is limited because the agent can only reach tumors that reside on or just under the skin/lining of internal organs. Moreover, the efficacy of PDT relies on the presence of oxygen; hypoxic tumor environments can reduce the effectiveness of the treatment. In conventional cancer therapies, advanced imaging is used to map the tumor’s location and shape. Techniques such as Intensity-Modulated Radiation Therapy (IMRT) and Stereotactic Radiosurgery (SRS) aim to deliver high doses to the tumor while sparing surrounding tissue. However, the maximum dose that can be given is limited by the tolerance of healthy tissues. Overexposure can lead to severe side effects such as radiation necrosis, especially in critical areas like the brain (e.g., glioblastoma treatment). This approach involves modifying the treatment plan based on changes in the tumor size and position during the course of therapy, using frequent imaging, to ensure that the tumor site can be targeted without compromising the health of the surrounding tissue.
Side Effects and Invasiveness
Cancer treatments such as chemotherapy and radiation therapy are mainstays in the fight against various types of cancer. While these treatments can be highly effective in controlling and eradicating tumors, they are also associated with significant side effects and invasiveness that impact patient quality of life. Understanding these drawbacks is crucial for developing more targeted and less harmful therapeutic approaches. While chemotherapy drugs and radiation are designed to kill or slow the growth of cancer cells, they can also affect nearby healthy cells and harm the body’s production of new ones. Common side effects of radiotherapy include skin reactions, fatigue, organ toxicity, and secondary cancers. Radiotherapy also requires precise localization and delivery, often necessitating immobilization devices and multiple treatment sessions over several weeks. This prolonged treatment course can be physically and emotionally demanding for the patient. On the other hand, chemotherapy can produce side effects such as gastrointestinal toxicity, cardiotoxicity, and hematologic toxicity, further increasing the patient’s risk of fatigue, nausea, bleeding complications, and congestive heart failure. In addition, chemotherapy is typically administered intravenously, requiring frequent hospital visits for infusions. The invasive nature of repeated venous access may cause discomfort for the patient and may lead to complications such as phlebitis and infection.
Heterogeneity of Markers
Although certain biomarkers, such as estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptor 2 (HER2), are widely utilized in the diagnosis and management of various cancers, their limitations in facilitating a one-size-fits-all treatment approach are evident. These limitations arise from the heterogeneity of cancer at multiple levels, including molecular, genetic, and phenotypic aspects. Cancers, even those categorized under the same type or subtype, exhibit considerable molecular and genetic variability. For instance, breast cancer is classified into several subtypes based on common markers like ER, PR, and HER2, which dictate the likely response to a given treatment. Tumors that express HER2-positive cancer cells can be treated by trastuzumab, an antibody drug that attaches to the HER2 protein and blocks the receptors from receiving the growth signals (“Targeted Therapies for HER2-Positive Early Breast Cancer”, 2024). However, tumors that do not overexpress HER2 would not be affected by this treatment as they have alternative methods for promoting their own growth. Moreover, markers like ER and PR can be expressed at varying levels, influencing the efficacy of hormone therapies designed for ER-positive or PR-positive breast cancers.
Heterogeneity in expression level also inhibits other treatments such as TRAIL-targeting therapies that selectively induce apoptosis in tumor cells. Specifically, TRAIL binds to death receptors on the cell surface, primarily DR4 (TRAIL-R1) and DR5 (TRAIL R-2), which triggers apoptotic pathways which lead to programmed cell death, and various drugs can be used to mimic this signal. TRAIL receptor levels vary among many cancer types and clinical studies are necessary to observe patient response and success rate (Snajdauf et al., 2021)
Additionally, heterogeneity can occur as mutations within the same gene. KRAS mutations are present in over 90% of pancreatic ductal adenocarcinomas (PDAC) and are involved in tumor initiation and progression (“Pancreatic Ductal Adenocarcinoma - NCI, 2024”). These mutations make KRAS a viable option for targeted therapies. While breast cancer can be treated by targeting several different proteins as markers, PDAC reveals various mutations within the same protein. As KRAS mutations are heterogeneous, a wide range of mutations (e.g., KRAS G12D, G12V) may potentially influence the tumor's behavior and response to therapy. The KRAS G12C mutation, for instance, can only be targeted by the Sotorasib inhibitor, which represents less than 2% of cases. Targeting KRAS successfully is complicated by this variability and the fact that KRAS mutations drive the tumor's biology in complex ways (Lammert et al., 2024) This variability means that treatments effective against one molecular profile may not be effective against another, limiting the applicability of a universal treatment.
P53 mutations are also highly heterogeneous, both in terms of their location within the gene and their functional consequences. Mutations in the TP53 gene, which encodes p53, are found in approximately half of all human cancers, making it a highly attractive target for cancer therapy (Hamzehloie et al., 2012). Some mutations result in a complete loss of p53 function, while others lead to the production of a dysfunctional protein with dominant-negative effects or gain-of-function properties that contribute to tumor progression. This heterogeneity complicates the development of a one-size-fits-all therapeutic approach, necessitating the design of treatments that can address the specific type of p53 mutation present in each patient’s tumor.
Even within a single tumor, different regions can present distinct molecular characteristics. This intratumoral heterogeneity can affect the expression of common markers and lead to differential responses to treatments targeting these markers (Gerlinger et al., 2012) As a result, a treatment designed to target a common marker may be less effective if the marker is variably expressed within different tumor regions. Venturing beyond tumors, common markers are often not entirely specific to cancer and can be expressed in non-cancerous tissues or other conditions. For example, elevated levels of PSA (prostate-specific antigen) can be found in benign prostatic hyperplasia as well as prostate cancer complicating the application of PSA-targeted therapies (Brosman, 2023). The reliance on common cancer markers for developing a one-size-fits-all treatment is therefore constrained by several limitations, including tumor heterogeneity, variability in marker expression, resistance mechanisms, and lack of specificity.
Limited Clinical Data
Limited clinical data represents a significant setback in the development and application of large-scale cancer therapies. Despite substantial preclinical research and promising early-phase trials, the translation of these findings into effective, broad-based treatments is often obstructed by a lack of comprehensive clinical evidence. This limitation of well-grounded knowledge and clinical findings hinders the development and progression of novel treatment ideas and complicates the identification of which groups specifically benefit/do not benefit from certain treatments. This obstacle stems from several factors, including the variability in patient responses, the complexity of cancer biology, and the challenges in conducting large-scale, multi-center trials. Moreover, this makes it challenging to create new treatments to fill in the gaps and resolve the issues posed with current therapies because clinical research at this stage has not been able to dig down to the root of such issues. In some cases, as with dendritic cell-based immunotherapy, the preparation process is technically complex, labor-intensive, and time-consuming, which can delay treatment and increase costs. Inadequate data can lead to an incomplete understanding of treatment efficacy and safety across diverse patient populations, which in turn impacts the ability to generalize findings and optimize therapeutic protocols. Moreover, limited clinical data can impede the identification of potential adverse effects and long-term outcomes, making it difficult to ensure that therapies are both effective and safe on a large scale. Addressing these gaps requires enhanced collaborative efforts, the inclusion of diverse patient cohorts, and the development of more robust clinical trial designs to generate the comprehensive data needed to support the widespread implementation of cancer therapies.
Future Directions
One of the most promising avenues in cancer research is the development and refinement of combination treatments. This approach leverages the synergistic effects of multiple treatment modalities to enhance efficacy and overcome resistance mechanisms that cancers often develop. Conventional treatments such as surgery, chemotherapy, and radiation therapy are increasingly being integrated with newer approaches like immunotherapy, targeted therapy, and gene therapy. For instance, the combination of immune checkpoint inhibitors with traditional chemotherapeutics has shown considerable promise in improving patient outcomes in various cancers, including melanoma and non-small cell lung cancer (NSCLC, Arriola et al., 2022)
By combining agents that work through distinct mechanisms, such as angiogenesis inhibitors with immune checkpoint inhibitors, researchers aim to create a more hostile environment for cancer cells, reducing their ability to adapt and survive.
The prospect of discovering a single cure for all cancers remains an area of significant debate within the scientific community. Cancer is not a single disease but a collection of over 100 distinct diseases, each characterized by different traits and expressions. This heterogeneity poses a formidable challenge to the concept of a universal cure. While advancements in understanding the common pathways involved in tumorigenesis, such as the p53 tumor suppressor pathway, offer some hope, the complexity and diversity of cancer types necessitate a multifaceted approach to treatment.
Recent breakthroughs in immunotherapy, particularly CAR-T cell therapy, have demonstrated that it is possible to achieve long-term remission in certain cancers, such as acute lymphoblastic leukemia (ALL) and some types of lymphoma (Sheykhhasan et al., 2022). However, these successes are currently limited to specific cancer subtypes, and the extension of such therapies to other malignancies requires overcoming significant scientific and clinical hurdles.
Moreover, the microenvironment of tumors, which includes immune cells, blood vessels, and stromal cells, adds another layer of complexity. Tumors can exploit these surrounding cells to support their growth and evade treatment, making it challenging to develop a one-size-fits-all approach due to the difficulty of managing and treating multiple sites once the tumor has metastasized. Therefore, while the dream of a universal cure for cancer is an inspiring goal, several obstacles still remain.
Nonetheless, there is great potential for further clinical research and discovery, especially in proteins such as p53 that are prevalent in numerous cancer types, and CAR T-cell therapy, which can be adapted to treat a variety of patients and cancer types. Future studies should focus on deepening our understanding of tumor heterogeneity, resistance mechanisms, and the tumor microenvironment to identify novel and universal therapeutic targets. Advanced techniques in genomics, proteomics, and systems biology could reveal new biomarkers and pathways crucial for developing successful treatments. Additionally, the improvement of artificial intelligence and imaging techniques holds promise in increasing the efficacy of current large-scale treatments such as radiotherapy. Clinical trials designed to test new treatment approaches and patient responses will be essential to overcoming the limitations of current treatments. By identifying larger patient populations, it is possible to test for positive indications and maximize the number of people that can benefit from a given treatment. By advancing these research areas, we can move closer to realizing the vision of a universal cancer therapy, optimizing outcomes, and improving survival rates across diverse patient populations.
The Personalized Approach
Definitions and Method
Personalized medicine is a rapidly growing practice of medicine that has seen several advancements and possible approaches in the treatment of cancer. Personalized therapies involve a comprehensive understanding of various factors – including inter-tumor and intra-tumor variability in genes, an individual’s biological and genetic makeup, tumor immune microenvironment, and the lifestyles and co-morbidities that vary among diverse patient groups and populations – to determine the best course of treatment for a patient. Therefore, a personalized treatment in oncology would require screening to pinpoint specific patient subgroups for clinical trials and tailored treatment. By using an individual’s unique genetic profile to guide decisions as to the treatment, prevention, and diagnosis of their particular type of cancer, a personalized treatment may prove to be more effective in eradicating cancer cells and addressing the personal needs of each patient for improved quality of life, more informed research, and a less randomized approach to testing which may lead to higher success rates.
Screening and Initial Therapy Selection
Personalized cancer treatment begins with extensive screening methods that utilize existing technologies to identify the most appropriate therapeutic approach for each patient, in consideration of their specific genomic and baseline characteristics (age and gender, type and stage of disease, etc.). Histology and Polymerase Chain Reaction (PCR) are crucial in this initial phase. Histological analysis involves the microscopic examination of tissue samples to detect cancerous cells, providing a fundamental understanding of the tumor's characteristics.
PCR plays a pivotal role by enabling whole-genome or specific target sequencing (Bernard & Wittwer, 2002). This method involves comparing the patient’s tissue with healthy tissue from the general population or adjacent healthy tissue from the same patient. Such comparisons help identify genetic mutations and other markers specific to the patient's cancer. For instance, in breast cancer, detailed histological and genetic analysis guides the selection of an initial treatment strategy that aligns with the unique features of the patient’s tumor.
Managing Resistance in Recurrent Tumors
A significant challenge in cancer treatment is the development of resistance in recurrent tumors. Addressing this issue requires a deep understanding of the underlying mechanisms that drive resistance. Radioresistance, for example, involves exploring DNA repair pathways, apoptosis, and cell cycle checkpoints. Radiotherapy exploits the accelerated growth common to all cancers; however, the resistance mechanisms can vary significantly between patients. By examining these pathways, researchers can identify specific mutations that contribute to resistance and devise secondary drug choices tailored to the patient's evolving cancer profile.
Preventative Treatment and Risk Factor Analysis
Furthermore, preventative treatment and risk factor assessment are essential components of personalized cancer care. Leveraging historical data from previous patients enables the identification of risk factors and the development of preventative treatments for future patients with similar cancer expressions. For example, analyzing the genetic profiles and treatment responses of past breast cancer patients can inform strategies to mitigate the risk of recurrence in new patients, guiding clinicians in making informed decisions about preventative measures.
Development of New Treatments
The advent of new technologies has ushered in a range of innovative treatments that significantly enhance the personalization of cancer therapy. mRNA vaccines, for instance, can be customized to the individual's cancer profile, stimulating the immune system to target and destroy cancer cells more effectively. For example, mRNA-4157, developed by Moderna, is designed to encode up to 34 neoantigens identified from the patient's tumor (Burris et al., 2019). Similarly, CAR T-cell therapy involves genetically modifying a patient's T-cells to express chimeric antigen receptors (CARs) that specifically recognize and attack cancer cells, offering a highly personalized and potent treatment option. While the approach can be considered more generalized, CAR T-cell therapy can be made personalized if the requirement for detailed molecular analysis through screening is in place to identify specific antigens present on the patient’s cancer cells. The method for the collection of T-cells is also personalized because T-cells are harvested from the patient themself. The modified T-cells are then expanded and re-infused into the patient, where they target and kill cancer cells (Olejarz et al., 2024). Therefore, the specificity of the CARs to the patient’s tumor antigens can be used as a very personalized treatment.
Additionally, the integration of extra data from patients' treatment responses into ongoing research efforts is crucial. This data-driven approach leads to the continuous refinement and development of more precise therapies. Personalized antibodies, designed to target specific tumor markers on cancer cells, represent another significant advancement, increasing the efficacy and specificity of cancer treatments. For example, trastuzumab specifically targets the HER2 receptor, which is overexpressed in certain breast cancers, blocking its activity and marking the cancer cells for destruction by the immune system.
Innovative Delivery Mechanisms
The delivery of personalized cancer treatments has also seen remarkable advancements. Nanobodies, due to their small size and high specificity, can be engineered to recognize and bind to tumor-specific surface markers, delivering treatments directly to cancer cells while minimizing damage to healthy tissue. For example, nanobodies targeting the HER2 receptor can deliver cytotoxic agents directly to HER2-positive breast cancer cells, sparing healthy tissue and reducing systemic toxicity. This treatment approach has seen tremendous success in inducing tumor regression in trastuzumab-resistant HER2-positive breast cancer (Deken et al., 2020). Advanced imaging techniques play a critical role in ensuring accurate delivery of these treatments. These techniques include fluorescent imaging that can guide surgeons in real-time during tumor removal, ensuring complete resection and minimizing damage to surrounding healthy tissue, and Magnetic Resonance Imaging (MRI), used to guide the delivery of magnetic nanobodies. These nanobodies can be directed to the tumor site using external magnetic fields, ensuring targeted delivery of therapeutic agents.
For example, imaging techniques can inform surgical procedures, aiding surgeons in precisely locating and removing tumors. Magnetic nanobodies, guided by external magnetic fields, offer a targeted approach to treatment delivery, enhancing precision. Drug implants that release therapeutic agents directly at the tumor site over time provide a consistent and controlled delivery method, ensuring sustained treatment efficacy. For instance, Gliadel wafers, which contain the chemotherapeutic agent carmustine, can be implanted in the surgical cavity after resection of glioblastoma. These wafers slowly release the drug, providing localized treatment and reducing the risk of systemic side effects (Perry et al., 2007).
Advantages
Personalized cancer treatments can offer significant advantages over traditional, one-size-fits-all approaches. These benefits include generating valuable data for further research, improving the likelihood of finding an effective treatment sooner, providing more specific information about treatment responses, and lowering the risk of recurrence. By tailoring therapies to the unique genetic and molecular profiles of individual patients, personalized treatments represent a significant advancement in oncology.
Generating Data for Further Research
Personalized cancer treatments generate extensive data that can be leveraged for further research, informing and enhancing screening methods and treatment strategies. The detailed genetic and molecular profiling conducted during personalized treatment helps identify tumor-specific mutations that can lead to the discovery of new drug targets. For instance, the identification of specific mutations in BRCA1 and BRCA2 genes has led to the development of PARP inhibitors, which exploit synthetic lethality to target cancer cells with these mutations. These genes are crucial for repairing double-strand DNA breaks through homologous recombination, and mutations in them impair this repair mechanism, making cells more prone to genetic errors that lead to cancer. PARP is involved in repairing single-strand DNA breaks through the base excision repair pathway. When PARP is inhibited, single-strand breaks accumulate and convert to double-strand breaks during DNA replication. In cells with functional BRCA1 or BRCA2, these breaks can be repaired, but in BRCA-mutated cancer cells, the defective repair mechanism leads to cell death. This selective killing of cancer cells with defective BRCA-mediated repair while sparing normal cells exemplifies synthetic lethality (Dedes et al., 2011). Clinically, PARP inhibitors such as olaparib, rucaparib, and niraparib have shown efficacy in treating BRCA-mutant ovarian, breast, and prostate cancers, offering a targeted treatment approach that minimizes damage to healthy cells and represents a significant advancement in precision medicine. Understanding the combinations of mutations that frequently occur together can provide insights into the molecular mechanisms of cancer, enabling the development of more effective treatments.
Moreover, personalized treatment data can reveal the efficacy of combining personalized therapies with more traditional treatments. For example, combining targeted therapies with chemotherapy or radiation can enhance treatment outcomes by attacking the cancer on multiple fronts. This approach can also help identify the genotypes that are more likely to experience certain side effects, enabling clinicians to tailor treatments to minimize adverse effects and improve patient quality of life.
Finding Effective Treatment Sooner
Personalized cancer treatments offer a logical approach that increases the chances of finding effective treatment sooner. By focusing on the specific genetic and molecular characteristics of a patient's tumor, personalized treatments reduce the need for multiple rounds of screening and treatment, streamlining the therapeutic process. This efficiency is not only easier for patients and clinical teams but also financially beneficial, as it reduces the costs associated with prolonged treatment and hospital stays.
For example, in cases of non-small cell lung cancer (NSCLC), patients with specific mutations in the EGFR (epithelial growth factor receptor) gene can be treated with EGFR inhibitors such as gefitinib or erlotinib. These inhibitors can counteract the permanent activation of the EGFR caused by the oncogenic mutations. Identifying these mutations early on allows for the immediate application of targeted therapies, bypassing the trial-and-error approach of traditional treatments and leading to quicker and more effective responses.
Specific Information of Treatment Responses
Personalized cancer treatments provide more specific information about why a treatment may have failed, allowing for better adjustments and more effective subsequent therapies. Detailed molecular profiling can pinpoint the exact cause of a lack of response, enabling clinicians to modify the treatment plan accordingly. This precise understanding facilitates the development of highly targeted therapies that address the specific resistance mechanisms present in the tumor.
For instance, in colorectal cancer, resistance to anti-EGFR therapy can often be attributed to mutations in the KRAS gene. By identifying these mutations through genetic testing, clinicians can switch to alternative treatments that are more likely to be effective, such as targeting the downstream pathways activated by KRAS mutations.
Lowering the Risk of Recurrence
Personalized cancer treatments can lower the risk of recurrence by targeting multiple aspects of the tumor's biology. By addressing a broad spectrum of genetic mutations and molecular pathways, personalized treatments can comprehensively eradicate cancer cells and prevent them from developing resistance. This approach reduces the likelihood of the cancer returning, providing long-term benefits for the patient.
For example, in breast cancer, combining hormonal therapies with targeted therapies such as CDK4/6 inhibitors (e.g. palbociclib) has shown to significantly reduce the risk of recurrence in patients with hormone receptor-positive breast cancer (Gil-Gil et al., 2021) By attacking the cancer cells through different mechanisms, this combination therapy ensures a more thorough eradication of the tumor.
Data-Driven Treatment Decisions
The integration of extensive patient data into the development and refinement of personalized treatments allows for continuous improvement in therapy selection and patient management. By analyzing large datasets from patients’ genetic profiles, treatment responses, and outcomes, researchers can identify patterns and correlations that inform the creation of increasingly precise and effective therapies. This data-driven approach not only enhances the current understanding of cancer biology but also facilitates the development of new treatment modalities tailored to specific patient populations. For example, the use of artificial intelligence and machine learning algorithms to analyze genomic data can predict which patients are likely to respond to particular therapies, leading to more informed and personalized treatment decisions.
Despite the promising advantages, personalized cancer treatments face several limitations that hinder their widespread implementation. These challenges include the setbacks associated with screening mechanisms, the lack of research on specific patient subsets, the limited availability of advanced technology, and the complexity of genetically engineering new treatments. Addressing these limitations is crucial for the continued advancement and success of personalized oncology.
Limitations
Setbacks of Screening Mechanisms
One of the significant limitations of personalized cancer treatments is the complexity and cost associated with screening mechanisms. Comprehensive genetic and molecular profiling is essential for identifying the unique characteristics of a patient's tumor, but this process can be prohibitively expensive. The high cost of sequencing technologies and the need for specialized reagents and equipment make it difficult to implement these screenings on a large scale. For example, whole-genome sequencing, which provides an in-depth analysis of a tumor's genetic landscape, can cost thousands of dollars per patient, limiting its accessibility in many healthcare settings.
In addition to the financial burden, the screening process is technically challenging and requires experienced staff. The interpretation of genetic data and the identification of actionable mutations demand a high level of expertise. Tools like MinION, a portable DNA sequencer, have made advancements in this field, but their operation and data analysis still require skilled personnel (Sakamoto et al., 2019). This need for specialized knowledge can strain healthcare systems and limit the availability of personalized treatments to institutions with the necessary resources and expertise.
Furthermore, screening mechanisms can be time-consuming, potentially delaying the initiation of treatment. The process of collecting, processing, and analyzing tumor samples can take several weeks, during which the patient's condition may deteriorate. The risk of prolonging the time to treatment can outweigh the benefits of personalized approaches, particularly in aggressive cancers where swift intervention is critical. Additionally, the invasive nature of sample collection, such as biopsies, poses risks and discomfort to patients. One way to mitigate this limitation may be liquid biopsies, which analyze circulating tumor DNA in blood samples, offering a less invasive alternative; however, their accuracy and reliability need further validation.
Lack of Research on Specific Patient Subsets
Another limitation of personalized cancer treatments is the lack of research available on specific patient subsets. Cancer is a heterogeneous disease, and the genetic mutations driving tumor development can vary significantly between individuals. While significant progress has been made in identifying common mutations in cancers such as breast, lung, and colorectal cancer, less is known about the genetic drivers in rarer cancer types or specific patient populations. This lack of comprehensive data results in a scarcity of leads to follow, hindering the development of targeted therapies for these groups.
For example, rare cancers like cholangiocarcinoma (bile duct cancer) or subsets of sarcomas have limited genomic data available, making it challenging to identify potential therapeutic targets. As a result, patients with these rare cancers often have fewer treatment options and may not benefit from the advancements in personalized oncology that are available for more common cancer types. The cost and effort required to conduct large-scale genomic studies in these patient subsets further contribute to this research gap.
Limited Availability of Advanced Technology
The implementation of personalized cancer treatments is also constrained by the limited availability of advanced technology. Cutting-edge technologies such as next-generation sequencing (NGS), single-cell RNA sequencing, and high-throughput drug screening are essential for identifying and validating potential targets for personalized therapies. However, these technologies are not widely available in all healthcare settings due to their high cost and the need for specialized infrastructure.
For instance, while NGS has revolutionized the field of genomics by enabling rapid and comprehensive sequencing of tumor genomes, its widespread adoption is limited by the substantial financial investment required for the equipment and maintenance. Additionally, the bioinformatics infrastructure needed to analyze and interpret the vast amounts of data generated by NGS is often lacking in many healthcare institutions (Paolillo et al., 2016). This technological gap creates disparities in access to personalized treatments, with only well-funded research centers and hospitals being able to offer these advanced diagnostic and therapeutic options.
Complexity of Genetically Engineering New Treatments
The development and implementation of genetically engineered treatments, such as CAR T-cell therapy, present significant challenges. CAR T-cell therapy involves modifying a patient's T cells to express chimeric antigen receptors (CARs) that target specific cancer antigens. While this approach has shown remarkable success in treating certain hematologic malignancies, its application is complex and labor-intensive.
The process of creating CAR T-cells involves several intricate steps, including the collection of T cells from the patient, genetic modification to introduce CARs, and expansion of the modified T cells before reinfusion into the patient. Each step requires precise control and optimization to ensure the safety and efficacy of the therapy. Furthermore, the manufacturing process is personalized for each patient, making it time-consuming and costly. For example, the production of CAR T-cells can take several weeks and cost hundreds of thousands of dollars per patient, posing significant logistical and financial challenges.
Additionally, the complexity of genetically engineering treatments extends to ensuring their safety and minimizing potential adverse effects. CAR T-cell therapy can cause severe side effects, such as cytokine release syndrome (CRS) and neurotoxicity, which require careful monitoring and management. The need for specialized facilities and personnel to handle these complications further limits the widespread availability of these advanced therapies.
Future Directions
Expanding Databases
Personalized treatments not only enhance the precision of care but also generate valuable data for ongoing research since the larger quantity of screening provides researchers more data points on cancer and specifically more data on specific mutations. This routine screening data can be incorporated into large databases which can inform future research niches and needs. This is a crucial step for understanding cancer's heterogeneity and behavior to develop new treatment strategies.
Screening Efficiency
Despite the promise of individualized treatments, one of the primary obstacles is the efficiency of screening processes. Effective personalized treatment hinges on the ability to accurately identify specific genetic mutations and biomarkers in a timely manner. However, current screening methods can be time-consuming, expensive, and sometimes invasive. This inefficiency hampers the widespread adoption of personalized treatments, including many immunotherapy-based approaches (Zugazagoitia et al., 2016). To overcome this barrier, there is a pressing need for more research focused on developing faster, more cost-effective, and less invasive screening techniques. Advances in technologies such as liquid biopsies and next-generation sequencing hold potential to revolutionize screening, making it more accessible and reliable for patients.
Liquid biopsies are a significant improvement from traditional biopsies and the FDA has already approved them. Whereas traditional biopsy is invasive and slow, liquid biopsy simply requires a blood test. This blood test detects the presence of circulating tumor cells (CTC) and circulating tumor DNA (ctDNA) which detach from tumors or metastatic sites, both of which have been big areas of research as new biomarkers for screening (Alix-Panabières and Pantel, 2021). Liquid biopsy also opens up the ability for us to monitor the evolution of cancer in real-time and get a better view of tumor heterogeneity. Recent advances in technology, aided by the use of microfluidics, have improved our ability to analyze these biopsies, yet there are still barriers to widespread adoption in the clinic, namely the standardization of procedures for analysis (Poulet et al, 2019) alongside greater infrastructure investment (Febbo et al., 2024).
Another technique that suffers from these logistical barriers is NGS, but its improving accuracy, speed, and cost have led many labs to consider its adoption for routine diagnostic analysis. Additional infrastructure and investment from labs will make NGS adoption a reality (Meldrum et al., 2011).
Informing Operations and Minimizing Human Error
Improving the utilization of existing cancer treatments is another critical area for future research. Personalized approaches can enhance traditional treatments such as surgery by providing detailed information about tumor location, size, and genetic makeup via advanced screening. This information can guide surgeons, enabling more precise removal of cancer and minimizing the element of human error since more accurate analysis will be available. This analysis can also help better elucidate treatment options for patients through test results. By combining traditional methods with insights gained from genetic screening, doctors can develop more accurate and effective treatment plans.
Immunotherapy – Personalizing General Care
Immunology has become an area of overwhelming interest concerning personalized oncological research in recent years, and at the heart of the field is the adaptation of personalized approaches. Chimeric Antigen Receptor T-cell (CAR-T) therapy is a prime example of this evolution. CAR-T therapy involves modifying a patient's own T cells to express a receptor specific to cancer cells. These engineered T cells are then reintroduced into the patient's body, where they seek out and destroy cancer cells. Originally developed as a generalized treatment for certain blood cancers, CAR-T therapy has shown remarkable success and is now being adapted for use in a wider range of cancers through personalization.
By identifying specific antigens present on a patient’s cancer cells, CAR-T therapy can be customized to target those unique markers. This personalization enhances the precision and effectiveness of the treatment, reducing the likelihood of relapse and minimizing damage to healthy tissues. Ongoing research aims to expand the applicability of CAR-T therapy to solid tumors and other cancer types.
Discussion
While the advancements in personalized cancer treatments are groundbreaking, the high costs associated with research and implementation might not justify their widespread adoption, given the relatively low impact on the larger patient population. Personalized treatments require extensive genetic screening, therapeutic development, and individualized care plans, all of which significantly drive up costs. These expenses can be prohibitive, especially when considering that the vast majority of cancer patients currently receive standardized treatments. Therefore, it is crucial to evaluate whether the benefits of personalized medicine outweigh the financial and logistical burdens it imposes on the healthcare system.
Thus, improving existing treatment modalities could present a more efficient path forward. Rather than developing entirely new therapies, refining the delivery and efficacy of current drugs offers a promising alternative. Enhancements in drug delivery systems, such as the use of nanobodies or nanoparticles, could minimize the impact on healthy cells, thereby reducing side effects and improving patients' quality of life. This would enable clinics to reuse existing infrastructure and training, avoiding the associated costs with new technologies and treatment options and keeping prices affordable for a larger patient population. Indeed, research and treatments involving repurposing drugs would be made even cheaper by the lack of patents, dropping costs anywhere from 6.6% to 66% (Vondeling et al., 2018).
Despite the potential improvements to current therapies, we still need to consider whether we have fully maximized the efficacy of existing treatments like chemotherapy. While chemotherapy has been a cornerstone of cancer treatment for decades, there is evidence to suggest that its effectiveness varies widely among patient populations, not to mention its potentially debilitating side effects. Research indicates that there is still room for optimization, particularly in drug delivery and minimizing adverse reactions, but we still need to learn more about why some patient subsets don’t respond to these treatments if we want to improve further. Using databases and results from increased screening would help us identify the factor (or factors) behind differing success. Ultimately, the question remains whether these improvements in current care are significant enough to match the potential benefits of personalized medicine in terms of patient outcomes.
Screening is a critical component of personalized treatment, eliminating much of the trial-and-error associated with broader treatment options. However, it also introduces challenges such as the need for more comprehensive diagnostic information, increased training for healthcare providers, longer waiting times before treatment can commence, and potentially invasive methods of procuring samples. On the flip side, enhanced screening contributes valuable data that can drive research and improve our understanding of cancer biology. The double-edged sword that is screening underscores the need for balanced approaches that leverage its benefits while mitigating its drawbacks. Thus, using therapeutic approaches to find ways around these limitations should be considered further. For example, when surgery would be a first-line treatment option, regardless of tumor type, the excised tumor can then be used freely for tests without the need for additional biopsy, minimizing the overall invasiveness. Collecting and using these extracted tumors would provide us with a new wealth of data to collect.
This paper advocates for personalized treatments to be considered as an adjuvant therapy, combined with current broader modalities of treatment to increase the efficacy of both. This strategy would reduce the immediate need for comprehensive screening while ensuring that personalized approaches are employed when most needed, thus justifying their higher costs. For example, TRAIL-based treatments while on their own are really only effective for singular cancer types at best, combining TRAIL-treatment with chemo- and radiotherapy is effective and produces good safety profiles (Snajdauf et al., 2021). Radiotherapy has been found to increase cell sensitivity to TRAIL-based treatments, meaning that those persisting after irradiation are more likely to be removed by subsequent chemotherapy against the TRAIL pathway. The most significant result from these trials was that cross-resistant tumors are most often killed by the combined treatment (Mérino, 2007). Treatment plans such as these could be a way around the heterogeneity of cancer.
Tumors, while displaying vastly different phenotypic expressions, have the same biomolecular mechanisms underlying them. Using individualized treatments to manipulate tumor expression can help increase the efficacy of one-size-fits-all approaches by reducing/eliminating population subsets that do not respond well. Hence, it seems that research should focus on the combinations of already existing drugs to discover ways around current heterogeneity barriers; such an approach would greatly increase efficacy of treatment for the greater patient population while also cutting costs.
Conclusion
There has been much enthusiasm in recent years about the therapeutic potential offered by personalized medicine, particularly in the field of cancer research. While a one-size-fits all approach is ideal due to its mass producibility and efficiency, the generalized nature of such treatments often overlooks the complex and heterogeneous qualities of cancer which has led many in the research community today to delve into personalized care. Tailored therapeutic strategies that comprehensively analyze the unique genetic and molecular profiles of individual patients may potentially produce more effective treatment outcomes in a smaller time frame, with more information about specific patient subtypes to inform further research. However, current healthcare technologies cannot support personalized therapies affordably on a large scale, posing a barrier to the clinical implementation of a promising concept. While tumors may be heterogeneous in their expression, the mechanisms that operate their cellular functions are the same; by using personalized treatment methods to regulate these underlying mechanisms and create a common phenotype that can be effectively targeted by generalized care. By taking advantage of the existing resources and infrastructure for current large-scale treatments such as chemotherapy and radiation, the cost of incorporating personalized therapies can be significantly reduced while still targeting cancer cells more specifically. Thus, exciting as the evolving field of personalized treatment plans might be, the advantages of a one-size-fits-all approach should not be overlooked when addressing the practical limitations. It is therefore necessary to utilize the foundation offered by current generalized treatments to maximize the efficiency of newer, personalized advancements.
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