Leveraging Cancer Phenotypic Plasticity for Novel Treatment Strategies.

Cancer cells, like all other organisms, are adept at switching their phenotype to adjust to the changes in their environment. Thus, phenotypic plasticity is a quantitative trait that confers a fitness advantage to the cancer cell by altering its phenotype to suit environmental circumstances. Until recently, new traits, especially in cancer, were thought to arise due to genetic factors; however, it is now amply evident that such traits could also emerge non-genetically due to phenotypic plasticity. Furthermore, phenotypic plasticity of cancer cells contributes to phenotypic heterogeneity in the population, which is a major impediment in treating the disease. Finally, plasticity also impacts the group behavior of cancer cells, since competition and cooperation among multiple clonal groups within the population and the interactions they have with the tumor microenvironment also contribute to the evolution of drug resistance. Thus, understanding the mechanisms that cancer cells exploit to tailor their phenotypes at a systems level can aid the development of novel cancer therapeutics and treatment strategies. Here, we present our perspective on a team medicine-based approach to gain a deeper understanding of the phenomenon to develop new therapeutic strategies.

Emerging biomarkers and molecular targets for precision medicine in cervical cancer.

Cervical cancer remains a significant global health burden, necessitating innovative approaches for improved diagnostics and personalized treatment strategies. Precision medicine has emerged as a promising paradigm, leveraging biomarkers and molecular targets to tailor therapy to individual patients. This review explores the landscape of emerging biomarkers and molecular targets in cervical cancer, highlighting their potential implications for precision medicine. By integrating these biomarkers into comprehensive diagnostic algorithms, clinicians can identify high-risk patients at an earlier stage, enabling timely intervention and improved patient outcomes. Furthermore, the identification of specific molecular targets has paved the way for the development of targeted therapies aimed at disrupting key pathways implicated in cervical carcinogenesis. In conclusion, the evolving landscape of biomarkers and molecular targets presents exciting opportunities for advancing precision medicine in cervical cancer. By harnessing these insights, clinicians can optimize treatment selection, enhance patient outcomes, and ultimately transform the management of this devastating disease.

A Nexus between Genetic and Non-Genetic Mechanisms Guides KRAS Inhibitor Resistance in Lung Cancer.

Several studies in the last few years have determined that, in contrast to the prevailing dogma that drug resistance is simply due to Darwinian evolution-the selection of mutant clones in response to drug treatment-non-genetic changes can also lead to drug resistance whereby tolerant, reversible phenotypes are eventually relinquished by resistant, irreversible phenotypes. Here, using KRAS as a paradigm, we illustrate how this nexus between genetic and non-genetic mechanisms enables cancer cells to evade the harmful effects of drug treatment. We discuss how the conformational dynamics of the KRAS molecule, that includes intrinsically disordered regions, is influenced by the binding of the targeted therapies contributing to conformational noise and how this noise impacts the interaction of KRAS with partner proteins to rewire the protein interaction network. Thus, in response to drug treatment, reversible drug-tolerant phenotypes emerge via non-genetic mechanisms that eventually enable the emergence of irreversible resistant clones via genetic mutations. Furthermore, we also discuss the recent data demonstrating how combination therapy can help alleviate KRAS drug resistance in lung cancer, and how new treatment strategies based on evolutionary principles may help minimize or even preclude the emergence of drug resistance.

Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers.

Gynecological cancers including breast, cervical, ovarian, uterine, and vaginal, pose the greatest threat to world health, with early identification being crucial to patient outcomes and survival rates. The application of machine learning (ML) and artificial intelligence (AI) approaches to the study of gynecological cancer has shown potential to revolutionize cancer detection and diagnosis. The current review outlines the significant advancements, obstacles, and prospects brought about by AI and ML technologies in the timely identification and accurate diagnosis of different types of gynecological cancers. The AI-powered technologies can use genomic data to discover genetic alterations and biomarkers linked to a particular form of gynecologic cancer, assisting in the creation of targeted treatments. Furthermore, it has been shown that the potential benefits of AI and ML technologies in gynecologic tumors can greatly increase the accuracy and efficacy of cancer diagnosis, reduce diagnostic delays, and possibly eliminate the need for needless invasive operations. In conclusion, the review focused on the integrative part of AI and ML based tools and techniques in the early detection and exclusion of various cancer types; together with a collaborative coordination between research clinicians, data scientists, and regulatory authorities, which is suggested to realize the full potential of AI and ML in gynecologic cancer care.

Acquired resistance to KRAS G12C small-molecule inhibitors via genetic/nongenetic mechanisms in lung cancer.

Inherent or acquired resistance to sotorasib poses a substantialt challenge for NSCLC treatment. Here, we demonstrate that acquired resistance to sotorasib in isogenic cells correlated with increased expression of integrin ß4 (ITGB4), a component of the focal adhesion complex. Silencing ITGB4 in tolerant cells improved sotorasib sensitivity, while overexpressing ITGB4 enhanced tolerance to sotorasib by supporting AKT-mTOR bypass signaling. Chronic treatment with sotorasib induced WNT expression and activated the WNT/ß-catenin signaling pathway. Thus, silencing both ITGB4 and ß-catenin significantly improved sotorasib sensitivity in tolerant, acquired, and inherently resistant cells. In addition, the proteasome inhibitor carfilzomib (CFZ) exhibited synergism with sotorasib by down-regulating ITGB4 and ß-catenin expression. Furthermore, adagrasib phenocopies the combination effect of sotorasib and CFZ by suppressing KRAS activity and inhibiting cell cycle progression in inherently resistant cells. Overall, our findings unveil previously unrecognized nongenetic mechanisms underlying resistance to sotorasib and propose a promising treatment strategy to overcome resistance.

Regression of ovarian cancer xenografts by depleting or inhibiting RLIP.

Ovarian cancer (OC) is the most prevalent and deadly cancer of the female reproductive system. Women will continue to be impacted by OC-related morbidity and mortality. Despite the fact that chemotherapy with cisplatin is the main component as the first-line anticancer treatment for OC, chemoresistance and unfavorable side effects are important obstacles to effective treatment. Targets for effective cancer therapy are required for cancer cells but not for non-malignant cells because they are expressed differently in cancer cells compared to normal cells. Targets for cancer therapy should preferably be components that already exist in biochemical and signalling frameworks and that significantly contribute to the development of cancer or regulate the response to therapy. RLIP is an important mercapturic acid pathway transporter that is crucial for survival and therapy resistance in cancers, therefore, we examined the role of RLIP in regulating essential signalling proteins involved in relaying the inputs from upstream survival pathways and mechanisms contributing to chemo-radiotherapy resistance in OC. The findings of our research offer insight into a novel anticancer effect of RLIP depletion/inhibition on OC and might open up new therapeutic avenues for OC therapy.

Targeting ITGB4/SOX2-driven lung cancer stem cells using proteasome inhibitors.

This study investigates the role of integrin ß4 (ITGB4) and stemness-associated factor SOX2 in platinum resistance in lung squamous cell carcinoma (LUSC). The expression of SOX2 and ITGB4 is found to be high in all LUSC subtypes, but the impact of ITGB4 expression on overall patient survival varies by subtype. Cancer stem cells (CSCs) isolated from LUSC patients were found to be resistant to cisplatin, but knocking down ITGB4 or SOX2 sensitized them to cisplatin. Carfilzomib (CFZ) synergized with cisplatin and suppressed CSC growth by inhibiting ITGB4 and SOX2 expression. Additionally, CFZ was found to inhibit SOX2 expression epigenetically by inhibiting histone acetylation at the SOX2 promoter site. CFZ also suppressed the growth of SOX2-dependent small cell lung cancer cells in vitro and in vivo. The study highlights the unique function of CFZ as a transcriptional suppressor of SOX2, independent of its proteasome inhibitory function.

Recent Advancement in Breast Cancer Research: Insights from Model Organisms-Mouse Models to Zebrafish.

Animal models have been utilized for decades to investigate the causes of human diseases and provide platforms for testing novel therapies. Indeed, breakthrough advances in genetically engineered mouse (GEM) models and xenograft transplantation technologies have dramatically benefited in elucidating the mechanisms underlying the pathogenesis of multiple diseases, including cancer. The currently available GEM models have been employed to assess specific genetic changes that underlay many features of carcinogenesis, including variations in tumor cell proliferation, apoptosis, invasion, metastasis, angiogenesis, and drug resistance. In addition, mice models render it easier to locate tumor biomarkers for the recognition, prognosis, and surveillance of cancer progression and recurrence. Furthermore, the patient-derived xenograft (PDX) model, which involves the direct surgical transfer of fresh human tumor samples to immunodeficient mice, has contributed significantly to advancing the field of drug discovery and therapeutics. Here, we provide a synopsis of mouse and zebrafish models used in cancer research as well as an interdisciplinary 'Team Medicine' approach that has not only accelerated our understanding of varied aspects of carcinogenesis but has also been instrumental in developing novel therapeutic strategies.

Arising Novel Agents in Lung Cancer: Are Bispecifics and ADCs the New Paradigm?

Lung cancer is one of the most common cancers with the highest mortality. Non-small cell lung cancer (NSCLC) contributes to around 85% of lung cancer diagnoses (vs. 15% for small cell lung cancer). The treatment of NSCLC has vastly changed in the last two decades since the development of immunotherapy and targeted therapy against driver mutations. As is the nature of malignancy, cancer cells have acquired resistance to these treatments prompting an investigation into novel treatments and new targets. Bispecific antibodies, capable of targeting multiple substrates at once, and antibody-drug conjugates that can preferentially deliver chemotherapy to tumor cells are examples of this innovation. From our initial evaluation, both treatment modalities appear promising.

Precision oncology provides opportunities for targeting KRAS-inhibitor resistance.

Novel inhibitors targeting Kirsten rat sarcoma virus homolog (KRAS) KRASG12C in various cancers have shown good initial efficacy, but therapy-related drug resistance eventually occurs in most patients. It has become apparent that cancer cells not only rely on novel mutations that provide escape mechanisms, but about half of them become resistant in the absence of apparent genetic mutations. Redundancies within the KRAS signaling pathways and cross-talk between these pathways - as well as other canonical cancer-driving mechanisms - not only provide challenges but also present opportunities for drug development and targeted approaches. We discuss the challenges for the duality of KRAS inhibitor drug resistance with an additional focus on nongenetic mechanisms and the potential for patient-centered combination treatments.

Addressing the genetic/nongenetic duality in cancer with systems biology.

The dogma that cancer is a genetic disease is being questioned. Recent findings suggest that genetic/nongenetic duality is necessary for cancer progression. A think tank organized by the Shraman Foundation's Institute for Theoretical Biology compiled key challenges and opportunities that theoreticians, experimentalists, and clinicians can explore from a systems biology perspective to provide a better understanding of the disease as well as help discover new treatment options and therapeutic strategies.

A Systems Biology Approach for Addressing Cisplatin Resistance in Non-Small Cell Lung Cancer.

Translational research in medicine, defined as the transfer of knowledge and discovery from the basic sciences to the clinic, is typically achieved through interactions between members across scientific disciplines to overcome the traditional silos within the community. Thus, translational medicine underscores 'Team Medicine', the partnership between basic science researchers and clinicians focused on addressing a specific goal in medicine. Here, we highlight this concept from a City of Hope perspective. Using cisplatin resistance in non-small cell lung cancer (NSCLC) as a paradigm, we describe how basic research scientists, clinical research scientists, and medical oncologists, in true 'Team Science' spirit, addressed cisplatin resistance in NSCLC and identified a previously approved compound that is able to alleviate cisplatin resistance in NSCLC. Furthermore, we discuss how a 'Team Medicine' approach can help to elucidate the mechanisms of innate and acquired resistance in NSCLC and develop alternative strategies to overcome drug resistance.

Leveraging deep learning algorithms for synthetic data generation to design and analyze biological networks.

The use of synthetic data is gaining an increasingly prominent role in data and machine learning workflows to build better models and conduct analyses with greater statistical inference. In the domains of healthcare and biomedical research, synthetic data may be seen in structured and unstructured formats. Concomitant with the adoption of synthetic data, a sub-discipline of machine learning known as deep learning has taken the world by storm. At a larger scale, deep learning methods tend to outperform traditional methods in regression and classification tasks. These techniques are also used in generative modeling and are thus prime candidates for generating synthetic data in both structured and unstructured formats. Here, we emphasize the generation of synthetic data in healthcare and biomedical research using deep learning methods for unstructured data formats such as text and images. Deep learning methods leverage the neural network algorithm, and in the context of generative modeling, several neural network architectures can create new synthetic data for a problem at hand including, but not limited to, recurrent neural networks (RNNs), variational autoencoders (VAEs), and generative adversarial networks (GANs). To better understand these methods, we will look at specific case studies such as generating realistic clinical notes of a patient, the generation of synthetic DNA sequences, as well as to enrich experimental data collected during the study of heterotypic cultures of cancer cells.

Addressing Drug Resistance in Cancer: A Team Medicine Approach.

Drug resistance remains one of the major impediments to treating cancer. Although many patients respond well initially, resistance to therapy typically ensues. Several confounding factors appear to contribute to this challenge. Here, we first discuss some of the challenges associated with drug resistance. We then discuss how a 'Team Medicine' approach, involving an interdisciplinary team of basic scientists working together with clinicians, has uncovered new therapeutic strategies. These strategies, referred to as intermittent or 'adaptive' therapy, which are based on eco-evolutionary principles, have met with remarkable success in potentially precluding or delaying the emergence of drug resistance in several cancers. Incorporating such treatment strategies into clinical protocols could potentially enhance the precision of delivering personalized medicine to patients. Furthermore, reaching out to patients in the network of hospitals affiliated with leading academic centers could help them benefit from such innovative treatment options. Finally, lowering the dose of the drug and its frequency (because of intermittent rather than continuous therapy) can also have a significant impact on lowering the toxicity and undesirable side effects of the drugs while lowering the financial burden carried by the patient and insurance providers.

Chemokines and NSCLC: Emerging role in prognosis, heterogeneity, and therapeutics.

Lung cancer persists to contribute to one-quarter of cancer-associated deaths. Among the different histologies, non-small cell lung cancer (NSCLC) alone accounts for 85% of the cases. The development of therapies involving immune checkpoint inhibitors and angiogenesis inhibitors has increased patients' survival probability and reduced mortality rates. Developing targeted therapies against essential genetic alterations also translates to better treatment strategies. But the benefits still seem farfetched due to the development of drug resistance and refractory tumors. In this review, we have highlighted the interplay of different tumor microenvironment components, essentially discussing the chemokine families (CC, CXC, C, and CX3C) that regulate the tumor biology in NSCLC and promote tumor growth, metastasis, and associated heterogeneity. The development of therapeutics and prognostic markers is a complex and multipronged approach. However, some essential chemokines can act as critical players for being considered potential prognostic markers and therapeutic targets.

Association between Immunosuppressive Therapy Utilized in the Treatment of Autoimmune Disease or Transplant and Cancer Progression.

Autoimmunity and cancer rates have both been on the rise in Western civilization prompting many to investigate the link between the two entities. This review will investigate the complex interactions between the activation and deactivation of the immune system and the development of malignancy. Additional focus will be placed on the main classes of immune inhibitor therapy utilized in transplant patients and in autoimmune disease including TNF-alpha, Calcineurin, mTOR, purine synthesis antagonists and IMPDH inhibitors.

Targeting CA-125 Transcription by Development of a Conditionally Replicative Adenovirus for Ovarian Cancer Treatment.

CA-125, encoded by the MUC16 gene, is highly expressed in most ovarian cancer cells and thus serves as a tumor marker for monitoring disease progression or treatment response in ovarian cancer patients. However, targeting MUC16/CA-125 for ovarian cancer treatment has not been successful to date. In the current study, we performed multiple steps of high-fidelity PCR and obtained a 5 kb DNA fragment upstream of the human MUC16 gene. Reporter assays indicate that this DNA fragment possesses transactivation activity in CA-125-high cancer cells, but not in CA-125-low cancer cells, indicating that the DNA fragment contains the transactivation region that controls specific expression of the MUC16 gene in ovarian cancer cells. We further refined the promoter and found a 1040 bp fragment with similar transcriptional activity and specificity. We used this refined MUC16 promoter to replace the E1A promoter in the adenovirus type 5 genome DNA, where E1A is an essential gene for adenovirus replication. We then generated a conditionally replicative oncolytic adenovirus (CRAd) that replicates in and lyses CA-125-high cancer cells, but not CA-125-low or -negative cancer cells. In vivo studies showed that intraperitoneal virus injection prolonged the survival of NSG mice inoculated intraperitoneally (ip) with selected ovarian cancer cell lines. Furthermore, the CRAd replicates in and lyses primary ovarian cancer cells, but not normal cells, collected from ovarian cancer patients. Collectively, these data indicate that targeting MUC16 transactivation utilizing CRAd is a feasible approach for ovarian cancer treatment that warrants further investigation.

Group Behavior and Emergence of Cancer Drug Resistance.

Drug resistance is a major impediment in cancer. Although it is generally thought that acquired drug resistance is due to genetic mutations, emerging evidence indicates that nongenetic mechanisms also play an important role. Resistance emerges through a complex interplay of clonal groups within a heterogeneous tumor and the surrounding microenvironment. Traits such as phenotypic plasticity, intercellular communication, and adaptive stress response, act in concert to ensure survival of intermediate reversible phenotypes, until permanent, resistant clones can emerge. Understanding the role of group behavior, and the underlying nongenetic mechanisms, can lead to more efficacious treatment designs and minimize or delay emergence of resistance.

RLIP depletion induces apoptosis associated with inhibition of JAK2/STAT3 signaling in melanoma cells.

The incidence of malignant melanoma, a neoplasm of melanocytic cells, is increasing rapidly. The lymph nodes are often the first site of metastasis and can herald systemic dissemination, which is almost uniformly fatal. RLIP, a multi-specific ATP-dependent transporter that is over-expressed in several types of cancers, plays a central role in cancer cell resistance to radiation and chemotherapy. RLIP appears to be necessary for cancer cell survival because both in vitro cell culture and in vivo animal tumor studies show that the depletion or inhibition of RLIP causes selective toxicity to malignant cells. RLIP depletion/inhibition triggers apoptosis in cancer cells by inducing the accumulation of endogenously formed glutathione-conjugates. In our in vivo studies, we administered RLIP antibodies or antisense oligonucleotides to mice bearing subcutaneous xenografts of SKMEL2 and SKMEL5 melanoma cells and demonstrated that both treatments caused significant xenograft regression with no apparent toxic effects. Anti-RLIP antibodies and antisense, which respectively inhibit RLIP-mediated transport and deplete RLIP expression, showed similar tumor regressing activities, indicating that the inhibition of RLIP transport activity at the cell surface is sufficient to achieve anti-tumor activity. Furthermore, RLIP antisense treatment reduced levels of RLIP, pSTAT3, pJAK2, pSrc, Mcl-1 and Bcl2, as well as CDK4 and cyclin B1, and increased levels of Bax and phospho 5' AMP-activated protein kinase (pAMPK). These studies indicate that RLIP serves as a key effector in the survival of melanoma cells and is a valid target for cancer therapy. Overall, compounds that inhibit, deplete or downregulate RLIP will function as wide-spectrum agents to treat melanoma, independent of common signaling pathway mutations.

Dynamic Phenotypic Switching and Group Behavior Help Non-Small Cell Lung Cancer Cells Evade Chemotherapy.

Drug resistance, a major challenge in cancer therapy, is typically attributed to mutations and genetic heterogeneity. Emerging evidence suggests that dynamic cellular interactions and group behavior also contribute to drug resistance. However, the underlying mechanisms remain poorly understood. Here, we present a new mathematical approach with game theoretical underpinnings that we developed to model real-time growth data of non-small cell lung cancer (NSCLC) cells and discern patterns in response to treatment with cisplatin. We show that the cisplatin-sensitive and cisplatin-tolerant NSCLC cells, when co-cultured in the absence or presence of the drug, display dynamic group behavior strategies. Tolerant cells exhibit a 'persister-like' behavior and are attenuated by sensitive cells; they also appear to 'educate' sensitive cells to evade chemotherapy. Further, tolerant cells can switch phenotypes to become sensitive, especially at low cisplatin concentrations. Finally, switching treatment from continuous to an intermittent regimen can attenuate the emergence of tolerant cells, suggesting that intermittent chemotherapy may improve outcomes in lung cancer.