Limits to Personalized Cancer Medicine

Multitargeted polypharmacotherapy for cancer treatment. theoretical concepts and proposals

INTRODUCTION

The pharmacological treatment of cancer has evolved from cytotoxic to molecular targeted therapy. The median survival gains of 124 drugs approved by the FDA from 2003 to 2021 is 2.8 months. Targeted therapy is based on the somatic mutation theory, which has some paradoxes and limitations. While efforts of targeted therapy must continue, we must study newer approaches that could advance therapy and affordability for patients.

AREAS COVERED

This work briefly overviews how cancer therapy has evolved from cytotoxic chemotherapy to current molecular-targeted therapy. The limitations of the one-target, one-drug approach considering cancer as a robust system and the basis for multitargeting approach with polypharmacotherapy using repurposing drugs.

EXPERT OPINION

Multitargeted polypharmacotherapy for cancer with repurposed drugs should be systematically investigated in preclinical and clinical studies. Remarkably, most of these proposed drugs already have a long history in the clinical setting, and their safety is known. In principle, the risk of their simultaneous administration should not be greater than that of a first-in-human phase I study as long as the protocol is developed with strict vigilance to detect early possible side effects from their potential interactions. Research on cancer therapy should go beyond the prevailing paradigm targeted therapy.

Targeting CD44 and other pleiotropic co-receptors as a means for broad inhibition of tumor growth and metastasis

Although progress has been made in the treatment of cancer, particularly for the four major types of cancers affecting the lungs, colon, breast and prostate, resistance to cancer treatment often emerges upon inhibition of major signaling pathways, which leads to the activation of additional pathways as a last-resort survival mechanism by the cancer cells. This signaling plasticity provides cancer cells with a level of operational freedom, reducing treatment efficacy. Plasticity is a characteristic of cancer cells that are not only able to switch signaling pathways but also from one cellular state (differentiated cells to stem cells or vice versa) to another. It seems implausible that the inhibition of one or a few signaling pathways of heterogeneous and plastic tumors can sustain a durable effect. We propose that inhibiting molecules with pleiotropic functions such as cell surface co-receptors can be a key to preventing therapy escape instead of targeting bona fide receptors. Therefore, we ask the question whether co-receptors often considered as "accessory molecules" are an overlooked key to control cancer cell behavior.

NGS-Guided Precision Oncology in Breast Cancer and Gynecological Tumors-A Retrospective Molecular Tumor Board Analysis

Background: Precision oncology treatments are being applied more commonly in breast and gynecological oncology through the implementation of Molecular Tumor Boards (MTBs), but real-world clinical outcome data remain limited. Methods: A retrospective analysis was conducted in patients with breast cancer (BC) and gynecological malignancies referred to our center's MTB from 2018 to 2023. The analysis covered patient characteristics, next-generation sequencing (NGS) results, MTB recommendations, therapy received, and clinical outcomes. Results: Sixty-three patients (77.8%) had metastatic disease, and forty-four patients (54.3%) had previously undergone three or more lines of systemic treatment. Personalized treatment recommendations were provided to 50 patients (63.3%), while 29 (36.7%) had no actionable target. Ultimately, 23 patients (29.1%) underwent molecular-matched treatment (MMT). Commonly altered genes in patients with pan-gyn tumors (BC and gynecological malignancies) included TP53 (n = 42/81, 51.9%), PIK3CA (n = 18/81, 22.2%), BRCA1/2 (n = 10/81, 12.3%), and ARID1A (n = 9/81, 11.1%). Patients treated with MMT showed significantly prolonged progression-free survival (median PFS 5.5 vs. 3.5 months, p = 0.0014). Of all patients who underwent molecular profiling, 13.6% experienced a major clinical benefit (PFSr ≥ 1.3 and PR/SD ≥ 6 months) through precision oncology. Conclusions: NGS-guided precision oncology demonstrated improved clinical outcomes in a subgroup of patients with gynecological and breast cancers.

Functional Plasmonic Microscope: Characterizing the Metabolic Activity of Single Cells via Sub-nm Membrane Fluctuations

Metabolic abnormalities are at the center of many diseases, and the capability to film and quantify the metabolic activities of a single cell is important for understanding the heterogeneities in these abnormalities. In this paper, a functional plasmonic microscope (FPM) is used to image and measure metabolic activities without fluorescent labels at a single-cell level. The FPM can accurately image and quantify the subnanometer membrane fluctuations with a spatial resolution of 0.5 μm in real time. These active cell membrane fluctuations are caused by metabolic activities across the cell membrane. A three-dimensional (3D) morphology of the bottom cell membrane was imaged and reconstructed with FPM to illustrate the capability of the microscope for cell membrane characterization. Then, the subnanometer cell membrane fluctuations of single cells were imaged and quantified with the FPM using HeLa cells. Cell metabolic heterogeneity is analyzed based on membrane fluctuations of each individual cell that is exposed to similar environmental conditions. In addition, we demonstrated that the FPM could be used to evaluate the therapeutic responses of metabolic inhibitors (glycolysis pathway inhibitor STF 31) on a single-cell level. The result showed that the metabolic activities significantly decrease over time, but the nature of this response varies, depicting cell heterogeneity. A low-concentration dose showed a reduced fluctuation frequency with consistent fluctuation amplitudes, while the high-concentration dose showcased a decreasing trend in both cases. These results have demonstrated the capabilities of the functional plasmonic microscope to measure and quantify metabolic activities for drug discovery.

Primary Results of Patients with Genitourinary Malignancies Presented at a Molecular Tumor Board

INTRODUCTION

Personalized medicine poses great opportunities and challenges. While the therapeutic landscape markedly expands, descriptions about status, clinical implementation and real-world benefits of precision oncology and molecular tumor boards (MTB) remain sparse, particularly in the field of genitourinary (GU) cancer. Hence, this study characterized urological MTB cases to better understand the potential role of MTB in uro-oncology.

METHODS

We analyzed patients with complete data sets being reviewed at an MTB from January 2019 to October 2022, focusing on results of molecular analysis and treatment recommendations.

RESULTS

We evaluated 102 patients with GU cancer with a mean patient age of 61.7 years. Prostate cancer (PCa) was the most frequent entity with 52.9% (54/102), followed by bladder cancer (18.6%, 19/102) and renal cell carcinoma (14.7%, 15/102). On average, case presentation at MTB took place 54.9 months after initial diagnosis and after 2.7 previous lines of therapy. During the study period, 49.0% (50/102) of patients deceased. Additional MTB-based treatment recommendations were achieved in a majority of 68.6% (70/102) of patients, with a recommendation for targeted therapy in 64.3% (45/70) of these patients. Only 6.7% (3/45) of patients - due to different reasons - received the recommended MTB-based therapy though, with 33% (1/3) of patients reaching disease control. Throughout the MTB study period, GU cancer case presentations and treatment recommendations increased, while the time interval between initial presentation and final therapy recommendation were decreasing over time.

CONCLUSION

Presentation of uro-oncological patients at the MTB is a highly valuable measure for clinical decision-making. Prospectively, earlier presentation of patients at the MTB and changing legislative issues regarding comprehensive molecular testing and targeted treatment approval might further improve patients' benefits from comprehensive molecular diagnostics.

Decitabine induces IRF7-mediated immune responses in p53-mutated triple-negative breast cancer: a clinical and translational study

p53 is mutated in half of cancer cases. However, no p53-targeting drugs have been approved. Here, we reposition decitabine for triple-negative breast cancer (TNBC), a subtype with frequent p53 mutations and extremely poor prognosis. In a retrospective study on tissue microarrays with 132 TNBC cases, DNMT1 overexpression was associated with p53 mutations (P = 0.037) and poor overall survival (OS) (P = 0.010). In a prospective DEciTabinE and Carboplatin in TNBC (DETECT) trial (NCT03295552), decitabine with carboplatin produced an objective response rate (ORR) of 42% in 12 patients with stage IV TNBC. Among the 9 trialed patients with available TP53 sequencing results, the 6 patients with p53 mutations had higher ORR (3/6 vs. 0/3) and better OS (16.0 vs. 4.0 months) than the patients with wild-type p53. In a mechanistic study, isogenic TNBC cell lines harboring DETECT-derived p53 mutations exhibited higher DNMT1 expression and decitabine sensitivity than the cell line with wild-type p53. In the DETECT trial, decitabine induced strong immune responses featuring the striking upregulation of the innate immune player IRF7 in the p53-mutated TNBC cell line (upregulation by 16-fold) and the most responsive patient with TNBC. Our integrative studies reveal the potential of repurposing decitabine for the treatment of p53-mutated TNBC and suggest IRF7 as a potential biomarker for decitabine-based treatments.

Measuring Progress in Precision Oncology

SUMMARY

In this issue of Cancer Discovery, Suehnholz and colleagues describe their efforts to quantify the gradual yet steady progress of precision oncology by surveying the regulatory approvals of targeted cancer therapies, and thus the actionability of corresponding molecular alterations in clinical practice, over more than 20 years. Their work also suggests a relationship between the discovery of candidate therapeutic targets through comprehensive tumor profiling and molecularly guided cancer drug development. See related article by Suehnholz et al., p. 49 (5).

Cellular Mass Response to Therapy Correlates With Clinical Response for a Range of Malignancies

PURPOSE

Cancer patients with advanced-stage disease have poor prognosis, typically having limited options for efficacious treatment, and genomics-based therapy guidance continues to benefit only a fraction of patients. Next-generation ex vivo approaches, such as cell mass-based response testing (MRT), offer an alternative precision medicine approach for a broader population of patients with cancer, but validation of clinical feasibility and potential impact remain necessary.

MATERIALS AND METHODS

We evaluated the clinical feasibility and accuracy of using live-cell MRT to predict patient drug sensitivity. Using a unified measurement workflow with a 48-hour result turnaround time, samples were subjected to MRT after treatment with a panel of drugs in vitro. After completion of therapeutic course, clinical response data were correlated with MRT-based predictions of outcome. Specimens were collected from 104 patients with solid (n = 69) and hematologic (n = 35) malignancies, using tissue formats including needle biopsies, malignant fluids, bone marrow aspirates, and blood samples. Of the 81 (78%) specimens qualified for MRT, 41 (51%) patients receiving physician-selected therapies had treatments matched to MRT.

RESULTS

MRT demonstrated high concordance with clinical responses with an odds ratio (OR) of 14.80 (P = .0003 [95% CI, 2.83 to 102.9]). This performance held for both solid and hematologic malignances with ORs of 20.67 (P = .0128 [95% CI, 1.45 to 1,375.57]) and 8.20 (P = .045 [95% CI, 0.77 to 133.56]), respectively. Overall, these results had a predictive accuracy of 80% (P = .0026 [95% CI, 65 to 91]).

CONCLUSION

MRT showed highly significant correlation with clinical response to therapy. Routine clinical use is technically feasible and broadly applicable to a wide range of samples and malignancy types, supporting the need for future validation studies.

Simple droplet microfluidics platform for drug screening on cancer spheroids

3D in vitro biological systems are progressively replacing 2D systems to increase the physiological relevance of cellular studies. Microfluidics-based approaches can be powerful tools towards such biomimetic systems, but often require high-end complicated and expensive processes and equipment for microfabrication. Herein, a drug screening platform is proposed, minimizing technicality and manufacturing steps. It provides an alternate way of spheroid generation in droplets in tubes. Droplet microfluidics then elicit multiple droplets merging events at programmable times, to submit sequentially the spheroids to chemotherapy and to reagents for cytotoxicity screening. After a comprehensive study of tumorogenesis within the droplets, the system is validated for drug screening (IC50) with chemotherapies in cancer cell lines as well as cells from a patient-derived-xenografts (PDX). As compared to microtiter plates methods, our system reduces the initial number of cells up to 10 times and opens new avenues towards primary tumors drug screening approaches.

Role of Tumor Molecular Profiling With FoundationOne®CDx in Advanced Solid Tumors: A Single-Centre Experience From Romania

Background In the field of precision oncology, comprehensive genomic profiling tests play a very important role by providing a complex understanding of the molecular characteristics of malignant tumors. Therefore, next-generation sequencing (NGS) has become a valuable tool in various aspects of cancer care from diagnosis and monitoring to treatment selection and personalized cancer treatment. Our aim was to evaluate the role of tumor molecular profiling in tailored treatment selection. Methods In our study, we conducted a retrospective analysis to assess the practicality of utilizing NGS testing in patients with metastatic solid tumors. The genomic testing was performed on blood or tissue samples from a fresh biopsy, less than six months old, and the expression of programmed death-ligand 1 was evaluated by immunohistochemistry. Results A total of 75 tests were performed on 66 patients between 2019 and 2022, with a success rate of 80%. The most common pathologies were gastro-intestinal tract cancer (26%), breast cancer (14%), non-small cell lung cancer (11%), and pancreatic cancer (11%). There were 9% liquid biopsies and 91% tissue biopsies. From all 66 patients tested, 55 had at least one genetic alteration. The most frequent genetic alteration found was TP53 (n=32) followed by KRAS (n=15) and BRCA1/2 (n=12) mutations. There were nine patients tested (14%) that presented a high tumor mutational burden, and only one patient presented high microsatellite instability. There were 37 patients (56%) with actionable alterations found from which 14 received matched therapy and four patients were enrolled in clinical trials. The NGS testing played a significant role in determining the next therapeutic strategy in 20 out of 66 patients (30.3%). Conclusion From all the patients included in our analysis, 83% had at least one mutation that is known to be of pathogenic significance but only 23% received treatment selected by the analysis of the tumor's genome, and only 6% were included in a clinical trial. This moderate success of personalized medicine using NGS testing highlights the importance of evaluating the factors that could lead to further improvement.

The Role of NCS1 in Immunotherapy and Prognosis of Human Cancer

The Neural Calcium Sensor1 (NCS1) is a crucial protein that binds to Ca2+ and is believed to play a role in regulating tumor invasion and cell proliferation. However, the role of NCS1 in immune infiltration and cancer prognosis is still unknown. Our study aimed to explore the expression profile, immune infiltration pattern, prognostic value, biological function, and potential compounds targeting NCS1 using public databases. High expression of NCS1 was detected by immune histochemical staining in LIHC (Liver hepatocellular carcinoma), BRCA (Breast invasive carcinoma), KIRC (Kidney renal clear cell carcinoma), and SKCM (Skin Cutaneous Melanoma). The expression of NCS1 in cancer was determined by TCGA (The Cancer Genome Atlas Program), GTEx (The Genotype-Tissue Expression), the Kaplan-Meier plotter, GEO (Gene Expression Omnibus), GEPIA2.0 (Gene Expression Profiling Interactive Analysis 2.0), HPA (The Human Protein Atlas), UALCAN, TIMER2.0, TISIDB, Metascape, Drugbank, chEMBL, and ICSDB databases. NCS1 has genomic mutations as well as aberrant DNA methylation in multiple cancers compared to normal tissues. Also, NCS1 was significantly different in the immune microenvironment, tumor mutational burden (TMB), microsatellite instability (MSI), and immune infiltrate-associated cells in different cancers, which could be used for the typing of immune and molecular subtypes of cancer and the presence of immune checkpoint resistance in several cancers. Univariate regression analysis, multivariate regression analysis, and gene enrichment analysis to construct prognostic models revealed that NCS1 is involved in immune regulation and can be used as a prognostic biomarker for SKCM, LIHC, BRCA, COAD, and KIRC. These results provide clues from a bioinformatic perspective and highlight the importance of NCS1 in a variety of cancers.

[Medical consent in the era of personalized medicine: Issues and recommendations]

Free and informed consent as a manifestation of adherence to a therapeutic act in medicine is central to the patient-physician relationship. Despite the medical advances of personalized medicine, it weakens the patient-physician relationship and thus the patient's capacity to consent, due to the increasing complexity of the analysis of available data and the intervention of a large number of specialist physicians in the care trajectory. This article proposes to examine the consequences of personalized medicine on the transmission and nature of information, to rethink the patient-physician relationship and the conditions under which consent is possible. Beyond the impacts of personalized medicine, we believe that the role of the doctor is similar to that of a coordinator capable of ensuring the transmission and coherence of information communicated to patients according to their needs with a view to restoring their understanding of the disease and the therapeutic proposals made to them.

Clinical Network Systems Biology: Traversing the Cancer Multiverse

In recent decades, cancer biology and medicine have ushered in a new age of precision medicine through high-throughput approaches that led to the development of novel targeted therapies and immunotherapies for different cancers. The availability of multifaceted high-throughput omics data has revealed that cancer, beyond its genomic heterogeneity, is a complex system of microenvironments, sub-clonal tumor populations, and a variety of other cell types that impinge on the genetic and non-genetic mechanisms underlying the disease. Thus, a systems approach to cancer biology has become instrumental in identifying the key components of tumor initiation, progression, and the eventual emergence of drug resistance. Through the union of clinical medicine and basic sciences, there has been a revolution in the development and approval of cancer therapeutic drug options including tyrosine kinase inhibitors, antibody-drug conjugates, and immunotherapy. This 'Team Medicine' approach within the cancer systems biology framework can be further improved upon through the development of high-throughput clinical trial models that utilize machine learning models, rapid sample processing to grow patient tumor cell cultures, test multiple therapeutic options and assign appropriate therapy to individual patients quickly and efficiently. The integration of systems biology into the clinical network would allow for rapid advances in personalized medicine that are often hindered by a lack of drug development and drug testing.

Determinants of targeted cancer therapy use in community oncology practice: a qualitative study using the Theoretical Domains Framework and Rummler-Brache process mapping

BACKGROUND

Precision medicine holds enormous potential to improve outcomes for cancer patients, offering improved rates of cancer control and quality of life. Not all patients who could benefit from targeted cancer therapy receive it, and some who may not benefit do receive targeted therapy. We sought to comprehensively identify determinants of targeted therapy use among community oncology programs, where most cancer patients receive their care.

METHODS

Guided by the Theoretical Domains Framework, we conducted semi-structured interviews with 24 community cancer care providers and mapped targeted therapy delivery across 11 cancer care delivery teams using a Rummler-Brache diagram. Transcripts were coded to the framework using template analysis, and inductive coding was used to identify key behaviors. Coding was revised until a consensus was reached.

RESULTS

Intention to deliver precision medicine was high across all participants interviewed, who also reported untenable knowledge demands. We identified distinctly different teams, processes, and determinants for (1) genomic test ordering and (2) delivery of targeted therapies. A key determinant of molecular testing was role alignment. The dominant expectation for oncologists to order and interpret genomic tests is at odds with their role as treatment decision-makers' and pathologists' typical role to stage tumors. Programs in which pathologists considered genomic test ordering as part of their staging responsibilities reported high and timely testing rates. Determinants of treatment delivery were contingent on resources and ability to offset delivery costs, which low- volume programs could not do. Rural programs faced additional treatment delivery challenges.

CONCLUSIONS

We identified novel determinants of targeted therapy delivery that potentially could be addressed through role re-alignment. Standardized, pathology-initiated genomic testing may prove fruitful in ensuring patients eligible for targeted therapy are identified, even if the care they need cannot be delivered at small and rural sites which may have distinct challenges in treatment delivery. Incorporating behavior specification and Rummler-Brache process mapping with determinant analysis may extend its usefulness beyond the identification of the need for contextual adaptation.

A Transcriptome-Based Precision Oncology Platform for Patient-Therapy Alignment in a Diverse Set of Treatment-Resistant Malignancies

Predicting in vivo response to antineoplastics remains an elusive challenge. We performed a first-of-kind evaluation of two transcriptome-based precision cancer medicine methodologies to predict tumor sensitivity to a comprehensive repertoire of clinically relevant oncology drugs, whose mechanism of action we experimentally assessed in cognate cell lines. We enrolled patients with histologically distinct, poor-prognosis malignancies who had progressed on multiple therapies, and developed low-passage, patient-derived xenograft models that were used to validate 35 patient-specific drug predictions. Both OncoTarget, which identifies high-affinity inhibitors of individual master regulator (MR) proteins, and OncoTreat, which identifies drugs that invert the transcriptional activity of hyperconnected MR modules, produced highly significant 30-day disease control rates (68% and 91%, respectively). Moreover, of 18 OncoTreat-predicted drugs, 15 induced the predicted MR-module activity inversion in vivo. Predicted drugs significantly outperformed antineoplastic drugs selected as unpredicted controls, suggesting these methods may substantively complement existing precision cancer medicine approaches, as also illustrated by a case study.

SIGNIFICANCE

Complementary precision cancer medicine paradigms are needed to broaden the clinical benefit realized through genetic profiling and immunotherapy. In this first-in-class application, we introduce two transcriptome-based tumor-agnostic systems biology tools to predict drug response in vivo. OncoTarget and OncoTreat are scalable for the design of basket and umbrella clinical trials. This article is highlighted in the In This Issue feature, p. 1275.

Anti-proliferation and induction of mitochondria-mediated apoptosis by Garcinia hanburyi resin in colorectal cancer cells

Introduction

Several parts of Garcinia hanburyi are used in traditional medicine for many purposes. In this study, Garcinia hanburyi resin (GHR) was explored for possible anti-proliferative effects and the underlying mechanism on colorectal cancer (CRC) cells.

Methods

Gambogic acid (GA) content in GHR was analyzed by HPLC method. The cytotoxicities of GA and GHR were assessed in human CRC cell lines (SW480 and Caco-2) and normal colon cells (CCD841 CoN) using a trypan blue exclusion assay, MTS assay, and cell morphology analysis. Cell cycle and apoptosis at its half maximal inhibitory concentration (IC50) were analyzed using flow cytometry. And, the levels of intrinsic apoptosis-related proteins were measured by Western blot analysis.

Results

GA was the major compound as 71.26% of the GHR. The cell viability of CRC cells was decreased in a time- and dose-dependent manner after exposure to GHR. The selectivity index indicated that GHR had a high degree of selectivity against CRC cells. The same result was obtained for GA treatment. In addition, GHR markedly induced typical apoptotic morphology of CRC cells, but had no obvious effect on normal colon cells. GHR induced apoptosis with the cell cycle arrest at the G2/M phase. An increase in Bax/Bcl-2 ratio and a decrease in procaspase-3 proteins indicated that GHR promoted apoptosis by disrupting the mitochondrial outer membrane permeability and the subsequent activation of caspase-3.

Conclusion

GHR, which contained GA as an active compound, significantly inhibited CRC cell proliferation via the induction of intrinsic apoptosis, while having low toxicity on normal colon cells. Therefore, GHR could be proposed as a potent candidate for the treatment of CRC.

Overview of the role of liquid biopsy in cancer management

With the emergence of novel targeted therapeutic options in early-stage and advanced-stage malignancies, researchers have shifted their focus on developing personalized treatment plans through molecular profiling. Circulating tumor DNA (ctDNA) is a cell-free DNA (ctDNA) fragment, originating from tumor cells, and circulating in the bloodstream as well as biological fluids. Over the past decade, many techniques were developed for liquid biopsies through next-generation sequencing. This alternative non-invasive biopsy offers several advantages in various types of tumors over traditional tissue biopsy. The process of liquid biopsy is considered minimally invasive and therefore easily repeatable when needed, providing a more dynamic analysis of the tumor cells. Moreover, it has an advantage in patients with tumors that are not candidates for tissue sampling. Besides, it offers a deeper understanding of tumor burden as well as treatment response, thereby enhancing the detection of minimal residual disease and therapeutic guidance for personalized medicine. Despite its many advantages, ctDNA and liquid biopsy do have some limitations. This paper discusses the basis of ctDNA and the current data available on the subject, as well as its clinical utility. We also reflect on the limitations of using ctDNA in addition to its future perspectives in clinical oncology and precision medicine.

An Inflection Point in Cancer Protein Biomarkers: What Was and What's Next

Biomarkers remain the highest value proposition in cancer medicine today - especially protein biomarkers. Yet despite decades of evolving regulatory frameworks to facilitate the review of emerging technologies, biomarkers have been mostly about promise with very little to show for improvements in human health. Cancer is an emergent property of a complex system and deconvoluting the integrative and dynamic nature of the overall system through biomarkers is a daunting proposition. The last two decades have seen an explosion of multi-omics profiling and a range of advanced technologies for precision medicine, including the emergence of liquid biopsy, exciting advances in single cell analysis, artificial intelligence (machine and deep learning) for data analysis and many other advanced technologies that promise to transform biomarker discovery. Combining multiple omics modalities to acquire a more comprehensive landscape of the disease state, we are increasingly developing biomarkers to support therapy selection and patient monitoring. Furthering precision medicine, especially in oncology, necessitates moving away from the lens of reductionist thinking towards viewing and understanding that complex diseases are, in fact, complex adaptive systems. As such, we believe it is necessary to re-define biomarkers as representations of biological system states at different hierarchical levels of biological order. This definition could include traditional molecular, histologic, radiographic, or physiological characteristics, as well as emerging classes of digital markers and complex algorithms. To succeed in the future, we must move past purely observational individual studies and instead start building a mechanistic framework to enable integrative analysis of new studies within the context of prior studies. Identifying information in complex systems and applying theoretical constructs, such as information theory, to study cancer as a disease of dysregulated communication could prove to be "game changing" for the clinical outcome of cancer patients.

Diverse rescue potencies of p53 mutations to ATO are predetermined by intrinsic mutational properties

Tumor suppressor p53 is inactivated by thousands of heterogeneous mutations in cancer, but their individual druggability remains largely elusive. Here, we evaluated 800 common p53 mutants for their rescue potencies by the representative generic rescue compound arsenic trioxide (ATO) in terms of transactivation activity, cell growth inhibition, and mouse tumor-suppressive activities. The rescue potencies were mainly determined by the solvent accessibility of the mutated residue, a key factor determining whether a mutation is a structural one, and the temperature sensitivity, the ability to reassemble the wild-type DNA binding surface at a low temperature, of the mutant protein. A total of 390 p53 mutants were rescued to varying degrees and thus were termed as type 1, type 2a, and type 2b mutations, depending on the degree to which they were rescued. The 33 type 1 mutations were rescued to amounts comparable to the wild type. In PDX mouse trials, ATO preferentially inhibited growth of tumors harboring type 1 and type 2a mutants. In an ATO clinical trial, we report the first-in-human mutant p53 reactivation in a patient harboring the type 1 V272M mutant. In 47 cell lines derived from 10 cancer types, ATO preferentially and effectively rescued type 1 and type 2a mutants, supporting the broad applicability of ATO in rescuing mutant p53. Our study provides the scientific and clinical communities with a resource of the druggabilities of numerous p53 mutations (www.rescuep53.net) and proposes a conceptual p53-targeting strategy based on individual mutant alleles rather than mutation type.

Tumor-Targeting Extracellular Vesicles Loaded with siS100A4 for Suppressing Postoperative Breast Cancer Metastasis

Introduction

S100A4 promotes the establishment of tumor microenvironment for malignant cancer cells, and knockdown of S100A4 can inhibit tumorigenesis. However, there is no efficient way to target S100A4 in metastatic tumor tissues. Here, we investigated the role of siS100A4-loaded iRGD-modified extracellular vesicles (siS100A4-iRGD-EVs) in postoperative breast cancer metastasis.

Methods

siS100A4-iRGD-EVs nanoparticles were engineered and analyzed using TEM and DLS. siRNA protection, cellular uptake, and cytotoxicity of EV nanoparticles were examined in vitro. Postoperative lung metastasis mouse model was created to investigate the tissue distribution and anti-metastasis roles of nanoparticles in vivo.

Results

siS100A4-iRGD-EVs protected siRNA from RNase degradation, enhanced the cellular uptake and compatibility in vitro. Strikingly, iRGD-modified EVs significantly increased tumor organotropism and siRNA accumulation in lung PMNs compared to siS100A4-EVs in vivo. Moreover, siS100A4-iRGD-EVs treatment remarkedly attenuated lung metastases from breast cancer and increased survival rate of mice through suppressing S100A4 expression in lung.

Conclusions

siS100A4-iRGD-EVs nanoparticles show more potent anti-metastasis effect in postoperative breast cancer metastasis mouse model.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12195-022-00757-5.

Liquid Biopsy-Based Volatile Organic Compounds from Blood and Urine and Their Combined Data Sets for Highly Accurate Detection of Cancer

Liquid biopsy is seen as a prospective tool for cancer screening and tracking. However, the difficulty lies in effectively sieving, isolating, and overseeing cancer biomarkers from the backdrop of multiple disrupting cells and substances. The current study reports on the ability to perform liquid biopsy without the need to physically filter and/or isolate the cancer cells per se. This has been achieved through the detection and classification of volatile organic compounds (VOCs) emitted from the cancer cells found in the headspace of blood or urine samples or a combined data set of both. Spectrometric analysis shows that blood and urine contain complementary or overlapping VOC information on kidney cancer, gastric cancer, lung cancer, and fibrogastroscopy subjects. Based on this information, a nanomaterial-based chemical sensor array in conjugation with machine learning as well as data fusion of the signals achieved was carried out on various body fluids to assess the VOC profiles of cancer. The detection of VOC patterns by either Gas Chromatography-Mass Spectrometry (GC-MS) analysis or our sensor array achieved >90% accuracy, >80% sensitivity, and >80% specificity in different binary classification tasks. The hybrid approach, namely, analyzing the VOC datasets of blood and urine together, contributes an additional discrimination ability to the improvement (>3%) of the model's accuracy. The contribution of the hybrid approach for an additional discrimination ability to the improvement of the model's accuracy is examined and reported.

Using patient-derived organoids to predict locally advanced or metastatic lung cancer tumor response: A real-world study

Predicting the clinical response to chemotherapeutic or targeted treatment in patients with locally advanced or metastatic lung cancer requires an accurate and affordable tool. Tumor organoids are a potential approach in precision medicine for predicting the clinical response to treatment. However, their clinical application in lung cancer has rarely been reported because of the difficulty in generating pure tumor organoids. In this study, we have generated 214 cancer organoids from 107 patients, of which 212 are lung cancer organoids (LCOs), primarily derived from malignant serous effusions. LCO-based drug sensitivity tests (LCO-DSTs) for chemotherapy and targeted therapy have been performed in a real-world study to predict the clinical response to the respective treatment. LCO-DSTs accurately predict the clinical response to treatment in this cohort of patients with advanced lung cancer. In conclusion, LCO-DST is a promising precision medicine tool in treating of advanced lung cancer.

Flotillin-1 palmitoylation turnover by APT-1 and ZDHHC-19 promotes cervical cancer progression by suppressing IGF-1 receptor desensitization and proteostasis

We have shown that insulin-like growth factor-1 (IGF-1) induces palmitoylation turnover of Flotillin-1 (Flot-1) in the plasma membrane (PM) for cell proliferation, after IGF-1 receptor (IGF-1R) signaling activation. However, the enzymes responsible for the turnover have not been identified. Herein, we show that acyl protein thioesterases-1 (APT-1) catalyzes Flot-1 depalmitoylation, and zinc finger DHHC domain-containing protein palmitoyltransferase-19 (ZDHHC-19) repalmitoylation of the depalmitoylated Flot-1 for the turnover in cervical cancer cells. The turnover prevented desensitization of IGF-1R via endocytosis and lysosomal degradation, thereby exerting excessive IGF-1R activation in cervical cancer cells. FLOT1, LYPLA1 and ZDHHC19 were highly expressed, and epithelial-to-mesenchymal transition (EMT)-inducing TIAM1 and GREM1 coordinately upregulated in malignant cervical cancer tissues. And blocking the turnover suppressed the EMT, migration, and invasion of cervical cancer cells. Our study identifies the specific enzymes regulating Flot-1 palmitoylation turnover, and reveals a novel regulatory mechanism of IGF-1-mediated cervical cancer progression.

Predictive biomarkers in gastric cancer

Predictive biomarkers are the mainstay of precision medicine. This review summarizes the advancements in tissue-based diagnostic biomarkers for gastric cancer, which is considered the leading cause of cancer-related deaths worldwide. A disease seen in the elderly, it is often diagnosed at an advanced stage, thereby limiting therapeutic options. In Western countries, neoadjuvant/perioperative (radio-)chemotherapy is administered, and adjuvant chemotherapy is administered in the East. The morpho-molecular classification of gastric cancer has opened novel avenues identifying Epstein-Barr-Virus (EBV)-positive, microsatellite instable, genomically stable and chromosomal instable gastric cancers. In chromosomal instable tumors, receptor tyrosine kinases (RKTs) (e.g., EGFR, FGFR2, HER2, and MET) are frequently overexpressed. Gastric cancers such as microsatellite instable and EBV-positive types often express immune checkpoint molecules, such as PD-L1 and VISTA. Genomically stable tumors show alterations in claudin 18.2. Next-generation sequencing is increasingly being used to search for druggable targets in advanced palliative settings. However, most tissue-based biomarkers of gastric cancer carry the risk of a sampling error due to intratumoral heterogeneity, and adequate tissue sampling is of paramount importance.

Next-Generation Sequencing for Advanced Breast Cancer: What the Way to Go?

The rapid implementation of precision medicine tools in diagnosing and treating breast cancer (BC) has widened the potential therapeutic options for patients. The applications of gene sequencing, including next-generation gene sequencing (NGS), have led to numerous questions on how to validate, implement, interpret, prioritize and operationalize precision medicine tools to deliver meaningful and impactful interventions. Limited benefit has been portended with earlier experiences of NGS-driven treatment, in BC. However, the development and use of frameworks of clinical actionability of genomic alterations, for example, detected with NGS, has resulted in better patient selection, and potentially higher therapeutic value. The European Society for Medical Oncology Scale for Clinical Actionability of molecular Targets (ESCAT) is a framework that includes five tiers of clinical actionability, with tier 1 reserved for approved drugs with demonstrated benefits for targetable genomic alterations. The re-analysis of clinical studies by grouping the genomic alterations and matched drugs with ESCAT, in high vs lower tiers has demonstrated a significant benefit portended by high tiers alterations, with the availability of efficacious treatments. As a result, frameworks for actionability, like ESCAT, should be fundamental in developing and implementing NGS-driven, and broadly, precision medicine research and treatments.

Integrated regulatory and metabolic networks of the tumor microenvironment for therapeutic target prioritization

Translation of genomic discovery, such as single-cell sequencing data, to clinical decisions remains a longstanding bottleneck in the field. Meanwhile, computational systems biological models, such as cellular metabolism models and cell signaling pathways, have emerged as powerful approaches to provide efficient predictions in metabolites and gene expression levels, respectively. However, there has been limited research on the integration between these two models. This work develops a methodology for integrating computational models of probabilistic gene regulatory networks with a constraint-based metabolism model. By using probabilistic reasoning with Bayesian Networks, we aim to predict cell-specific changes under different interventions, which are embedded into the constraint-based models of metabolism. Applications to single-cell sequencing data of glioblastoma brain tumors generate predictions about the effects of pharmaceutical interventions on the regulatory network and downstream metabolisms in different cell types from the tumor microenvironment. The model presents possible insights into treatments that could potentially suppress anaerobic metabolism in malignant cells with minimal impact on other cell types' metabolism. The proposed integrated model can guide therapeutic target prioritization, the formulation of combination therapies, and future drug discovery. This model integration framework is also generalizable to other applications, such as different cell types, organisms, and diseases.

Predictive validity in drug discovery: what it is, why it matters and how to improve it

Successful drug discovery is like finding oases of safety and efficacy in chemical and biological deserts. Screens in disease models, and other decision tools used in drug research and development (R&D), point towards oases when they score therapeutic candidates in a way that correlates with clinical utility in humans. Otherwise, they probably lead in the wrong direction. This line of thought can be quantified by using decision theory, in which 'predictive validity' is the correlation coefficient between the output of a decision tool and clinical utility across therapeutic candidates. Analyses based on this approach reveal that the detectability of good candidates is extremely sensitive to predictive validity, because the deserts are big and oases small. Both history and decision theory suggest that predictive validity is under-managed in drug R&D, not least because it is so hard to measure before projects succeed or fail later in the process. This article explains the influence of predictive validity on R&D productivity and discusses methods to evaluate and improve it, with the aim of supporting the application of more effective decision tools and catalysing investment in their creation.

Precision medicine and diseases as natural kinds: An epistemological dilemma

BACKGROUND

The most important advance of precision medicine (PM) has been a specific way to define and understand disease. However, PM may fail to be therapeutically effective if diseases are natural kinds.

OBJECTIVE

To attest adverse consequences of treatments suggested by PM that do not generalize well.

METHODS

Conceptual analysis of PM; Epistemology of clinical reasoning; Cases that show diseases as natural kinds to clash with epistemology of PM.

RESULTS

Contemplation of future research options that could clarify the position of PM under the conception of diseases as natural kinds.

CONCLUSION

Need for improved design of future interventions that better acknowledge problematic epistemology of PM.

How to read a next-generation sequencing report—what oncologists need to know

Next-generation sequencing (NGS) of tumor cell-derived DNA/RNA to screen for targetable genomic alterations is now widely available and has become part of routine practice in oncology. NGS testing strategies depend on cancer type, disease stage and the impact of results on treatment selection. The European Society for Medical Oncology (ESMO) has recently published recommendations for the use of NGS in patients with advanced cancer. We complement the ESMO recommendations with a practical review of how oncologists should read and interpret NGS reports. A concise and straightforward NGS report contains details of the tumor sample, the technology used and highlights not only the most important and potentially actionable results, but also other pathogenic alterations detected. Variants of unknown significance should also be listed. Interpretation of NGS reports should be a joint effort between molecular pathologists, tumor biologists and clinicians. Rather than relying and acting on the information provided by the NGS report, oncologists need to obtain a basic level of understanding to read and interpret NGS results. Comprehensive annotated databases are available for clinicians to review the information detailed in the NGS report. Molecular tumor boards do not only stimulate debate and exchange, but may also help to interpret challenging reports and to ensure continuing medical education.

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NGS is routinely carried out in the diagnostic work-up of several cancer types.

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In many other malignancies NGS is carried out after exhaustion of standard therapy options.

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Minimal requirements for the NGS report are detailed in this review.

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Interpretation of NGS reports can be challenging and require interdisciplinary discussion.

Nondestructive protein sampling with electroporation facilitates profiling of spatial differential protein expression in breast tumors in vivo

Excision tissue biopsy, while central to cancer treatment and precision medicine, presents risks to the patient and does not provide a sufficiently broad and faithful representation of the heterogeneity of solid tumors. Here we introduce e-biopsy—a novel concept for molecular profiling of solid tumors using molecular sampling with electroporation. As e-biopsy provides access to the molecular composition of a solid tumor by permeabilization of the cell membrane, it facilitates tumor diagnostics without tissue resection. Furthermore, thanks to its non tissue destructive characteristics, e-biopsy enables probing the solid tumor multiple times in several distinct locations in the same procedure, thereby enabling the spatial profiling of tumor molecular heterogeneity.We demonstrate e-biopsy in vivo, using the 4T1 breast cancer model in mice to assess its performance, as well as the inferred spatial differential protein expression. In particular, we show that proteomic profiles obtained via e-biopsy in vivo distinguish the tumors from healthy breast tissue and reflect spatial tumor differential protein expression. E-biopsy provides a completely new molecular sampling modality for solid tumors molecular cartography, providing information that potentially enables more rapid and sensitive detection at lesser risk, as well as more precise personalized medicine.

Cellular hierarchies predict drug response in acute myeloid leukemia

In a recent Nature Medicine study, Zeng and colleagues integrate both genomic and stem cell models of acute myeloid leukemia (AML) by deconvoluting cellular hierarchies of more than 1,000 AML samples. This work introduces a framework capable of predicting drug responses to targeted therapies in future clinical trials.

Clinical utility of whole-genome sequencing in precision oncology

Precision diagnostics is one of the two pillars of precision medicine. Sequencing efforts in the past decade have firmly established cancer as a primarily genetically driven disease. This concept is supported by therapeutic successes aimed at particular pathways that are perturbed by specific driver mutations in protein-coding domains and reflected in three recent FDA tissue agnostic cancer drug approvals. In addition, there is increasing evidence from studies that interrogate the entire genome by whole-genome sequencing that acquired global and complex genomic aberrations including those in non-coding regions of the genome might also reflect clinical outcome. After addressing technical, logistical, financial and ethical challenges, national initiatives now aim to introduce clinical whole-genome sequencing into real-world diagnostics as a rational and potentially cost-effective tool for response prediction in cancer and to identify patients who would benefit most from 'expensive' targeted therapies and recruitment into clinical trials. However, so far, this has not been accompanied by a systematic and prospective evaluation of the clinical utility of whole-genome sequencing within clinical trials of uniformly treated patients of defined clinical outcome. This approach would also greatly facilitate novel predictive biomarker discovery and validation, ultimately reducing size and duration of clinical trials and cost of drug development. This manuscript is the third in a series of three to review and critically appraise the potential and challenges of clinical whole-genome sequencing in solid tumors and hematological malignancies.

Risk and benefit for umbrella trials in oncology: a systematic review and meta-analysis

BACKGROUND

Umbrella clinical trials in precision oncology are designed to tailor therapies to the specific genetic changes within a tumor. Little is known about the risk/benefit ratio for umbrella clinical trials. The aim of our systematic review with meta-analysis was to evaluate the efficacy and safety profiles in cancer umbrella trials testing targeted drugs or a combination of targeted therapy with chemotherapy.

METHODS

Our study was prospectively registered in PROSPERO (CRD42020171494). We searched Embase and PubMed for cancer umbrella trials testing targeted agents or a combination of targeted therapies with chemotherapy. We included solid tumor studies published between 1 January 2006 and 7 October 2019. We measured the risk using drug-related grade 3 or higher adverse events (AEs), and the benefit by objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). When possible, data were meta-analyzed.

RESULTS

Of the 6207 records identified, we included 31 sub-trials or arms of nine umbrella trials (N = 1637). The pooled overall ORR was 17.7% (95% confidence interval [CI] 9.5-25.9). The ORR for targeted therapies in the experimental arms was significantly lower than the ORR for a combination of targeted therapy drugs with chemotherapy: 13.3% vs 39.0%; p = 0.005. The median PFS was 2.4 months (95% CI 1.9-2.9), and the median OS was 7.1 months (95% CI 6.1-8.4). The overall drug-related death rate (drug-related grade 5 AEs rate) was 0.8% (95% CI 0.3-1.4), and the average drug-related grade 3/4 AE rate per person was 0.45 (95% CI 0.40-0.50).

CONCLUSIONS

Our findings suggest that, on average, one in five cancer patients in umbrella trials published between 1 January 2006 and 7 October 2019 responded to a given therapy, while one in 125 died due to drug toxicity. Our findings do not support the expectation of increased patient benefit in cancer umbrella trials. Further studies should investigate whether umbrella trial design and the precision oncology approach improve patient outcomes.

Near-Infrared Light Regulation of Capture and Release of ctDNA Platforms Based on the DNA Assembly System

Despite recent progress, a challenge remains on how to gently release and recover viable ctDNA captured on DNA probe-based devices. Here, a reusable detector was successfully manufactured for the capture and release of ctDNA by means of an UCNPs@SiO2-Azo/CD-probe. Biocompatible NIR light is used to excite UCNPs and convert into local UV light. Continuous irradiation induces a rapid release of the entire ctDNA-probe–CD complex from the functionalized surface via the trans−cis isomerization of azo units without disrupting the ctDNA-structure receptor. Specifically, these composite chips allow reloading DNA probes for reusable ctDNA detection with no obvious influence on their efficiency. The results of our study demonstrated the potential application of this platform for the quantitative detection of ctDNA and the individualized analysis of cancer patients.

Individualized Prediction of Drug Response and Rational Combination Therapy in NSCLC Using Artificial Intelligence-Enabled Studies of Acute Phosphoproteomic Changes

We hypothesize that the study of acute protein perturbation in signal transduction by targeted anticancer drugs can predict drug sensitivity of these agents used as single agents and rational combination therapy. We assayed dynamic changes in 52 phosphoproteins caused by an acute exposure (1 hour) to clinically relevant concentrations of seven targeted anticancer drugs in 35 non-small cell lung cancer (NSCLC) cell lines and 16 samples of NSCLC cells isolated from pleural effusions. We studied drug sensitivities across 35 cell lines and synergy of combinations of all drugs in six cell lines (252 combinations). We developed orthogonal machine-learning approaches to predict drug response and rational combination therapy. Our methods predicted the most and least sensitive quartiles of drug sensitivity with an AUC of 0.79 and 0.78, respectively, whereas predictions based on mutations in three genes commonly known to predict response to the drug studied, for example, EGFR, PIK3CA, and KRAS, did not predict sensitivity (AUC of 0.5 across all quartiles). The machine-learning predictions of combinations that were compared with experimentally generated data showed a bias to the highest quartile of Bliss synergy scores (P = 0.0243). We confirmed feasibility of running such assays on 16 patient samples of freshly isolated NSCLC cells from pleural effusions. We have provided proof of concept for novel methods of using acute ex vivo exposure of cancer cells to targeted anticancer drugs to predict response as single agents or combinations. These approaches could complement current approaches using gene mutations/amplifications/rearrangements as biomarkers and demonstrate the utility of proteomics data to inform treatment selection in the clinic.

Colorectal cancer subtype identification from differential gene expression levels using minimalist deep learning

Background

Cancer molecular subtyping plays a critical role in individualized patient treatment. In previous studies, high-throughput gene expression signature-based methods have been proposed to identify cancer subtypes. Unfortunately, the existing ones suffer from the curse of dimensionality, data sparsity, and computational deficiency.

Methods

To address those problems, we propose a computational framework for colorectal cancer subtyping without any exploitation in model complexity and generality. A supervised learning framework based on deep learning (DeepCSD) is proposed to identify cancer subtypes. Specifically, based on the differentially expressed genes under cancer consensus molecular subtyping, we design a minimalist feed-forward neural network to capture the distinct molecular features in different cancer subtypes. To mitigate the overfitting phenomenon of deep learning as much as possible, L₁ and L₂ regularization and dropout layers are added.

Results

For demonstrating the effectiveness of DeepCSD, we compared it with other methods including Random Forest (RF), Deep forest (gcForest), support vector machine (SVM), XGBoost, and DeepCC on eight independent colorectal cancer datasets. The results reflect that DeepCSD can achieve superior performance over other algorithms. In addition, gene ontology enrichment and pathology analysis are conducted to reveal novel insights into the cancer subtype identification and characterization mechanisms.

Conclusions

DeepCSD considers all subtype-specific genes as input, which is pathologically necessary for its completeness. At the same time, DeepCSD shows remarkable robustness in handling cross-platform gene expression data, achieving similar performance on both training and test data without significant model overfitting or exploitation of model complexity.

Supplementary Information

The online version contains supplementary material available at (10.1186/s13040-022-00295-w).

Repurposing antiparasitic antimonials to noncovalently rescue temperature-sensitive p53 mutations

The tumor suppressor p53 is inactivated by over hundreds of heterogenous mutations in cancer. Here, we purposefully selected phenotypically reversible temperature-sensitive (TS) p53 mutations for pharmacological rescue with thermostability as the compound-screening readout. This rational screening identified antiparasitic drug potassium antimony tartrate (PAT) as an agent that can thermostabilize the representative TS mutant p53-V272M via noncovalent binding. PAT met the three basic criteria for a targeted drug: availability of a co-crystal structure, compatible structure-activity relationship, and intracellular target specificity, consequently exhibiting antitumor activity in a xenograft mouse model. At the antimony dose in clinical antiparasitic therapy, PAT effectively and specifically rescued p53-V272M in patient-derived primary leukemia cells in single-cell RNA sequencing. Further scanning of 815 frequent p53-missense mutations identified 65 potential PAT-treatable mutations, most of which were temperature sensitive. These results lay the groundwork for repurposing noncovalent antiparasitic antimonials for precisely treating cancers with the 65 p53 mutations.

American Cancer Society's report on the status of cancer disparities in the United States, 2021

In this report, the authors provide comprehensive and up-to-date US data on disparities in cancer occurrence, major risk factors, and access to and utilization of preventive measures and screening by sociodemographic characteristics. They also review programs and resources that have reduced cancer disparities and provide policy recommendations to further mitigate these inequalities. The overall cancer death rate is 19% higher among Black males than among White males. Black females also have a 12% higher overall cancer death rate than their White counterparts despite having an 8% lower incidence rate. There are also substantial variations in death rates for specific cancer types and in stage at diagnosis, survival, exposure to risk factors, and receipt of preventive measures and screening by race/ethnicity, socioeconomic status, and geographic location. For example, kidney cancer death rates by sex among American Indian/Alaska Native people are ≥64% higher than the corresponding rates in each of the other racial/ethnic groups, and the 5-year relative survival for all cancers combined is 14% lower among residents of poorer counties than among residents of more affluent counties. Broad and equitable implementation of evidence-based interventions, such as increasing health insurance coverage through Medicaid expansion or other initiatives, could substantially reduce cancer disparities. However, progress will require not only equitable local, state, and federal policies but also broad interdisciplinary engagement to elevate and address fundamental social inequities and longstanding systemic racism.

Prognostic and Theranostic Biomarkers in Ovarian Clear Cell Carcinoma

In this study, we aimed to test whether prognostic biomarkers can achieve a clinically relevant stratification of patients with stage I ovarian clear cell carcinoma (OCCC) and to survey the expression of 10 selected actionable targets (theranostic biomarkers) in stage II to IV cases. From the population-based Alberta Ovarian Tumor Type study, 160 samples of OCCC were evaluated by immunohistochemistry and/or silver-enhanced in situ hybridization for the status of 5 prognostic (p53, p16, IGF2BP3, CCNE1, FOLR1) and 10 theranostic biomarkers (ALK, BRAF V600E, ERBB2, ER, MET, MMR, PR, ROS1, NTRK1-3, VEGFR2). Kaplan-Meier survival analyses were performed. Cases with abnormal p53 or combined p16/IFG2BP3 abnormal expression identified a small subset of patients (6/54 cases) with stage I OCCC with an aggressive course (5-yr ovarian cancer-specific survival of 33.3%, compared with 91.5% in the other stage I cases). Among theranostic targets, ERBB2 amplification was present in 11/158 (7%) of OCCC, while MET was ubiquitously expressed in OCCC similar to a variety of normal control tissues. ER/PR showed a low prevalence of expression. No abnormal expression was detected for any of the other targets. We propose a combination of 3 biomarkers (p53, p16, IGF2BP3) to predict prognosis and the potential need for adjuvant therapy for patients with stage I OCCC. This finding requires replication in larger cohorts. In addition, OCCC could be tested for ERBB2 amplification for inclusion in gynecological basket trials targeting this alteration.

Machine learning for multi-omics data integration in cancer

Multi-omics data analysis is an important aspect of cancer molecular biology studies and has led to ground-breaking discoveries. Many efforts have been made to develop machine learning methods that automatically integrate omics data. Here, we review machine learning tools categorized as either general-purpose or task-specific, covering both supervised and unsupervised learning for integrative analysis of multi-omics data. We benchmark the performance of five machine learning approaches using data from the Cancer Cell Line Encyclopedia, reporting accuracy on cancer type classification and mean absolute error on drug response prediction, and evaluating runtime efficiency. This review provides recommendations to researchers regarding suitable machine learning method selection for their specific applications. It should also promote the development of novel machine learning methodologies for data integration, which will be essential for drug discovery, clinical trial design, and personalized treatments.

Genomic Alterations in Head and Neck Squamous Cell Carcinoma: Level of Evidence According to ESMO Scale for Clinical Actionability of Molecular Targets (ESCAT)

Development of high-throughput technologies helped to decipher tumor genomic landscapes revealing actionable molecular alterations. We aimed to rank the level of evidence of recurrent actionable molecular alterations in head and neck squamous cell carcinoma (HNSCC) on the basis of the European Society for Medical Oncology (ESMO) Scale for Clinical Actionability of Molecular Targets (ESCAT) to help the clinicians prioritize treatment. We identified actionable alterations in 33 genes. HRAS-activating mutations were ranked in tier IB because of the efficacy of tipifarnib (farnesyltransferase inhibitor) in HRAS-mutated patients with HNSCC (nonrandomized clinical trial). Microsatellite instability (MSI), high tumor mutational burden (TMB), and NTRK fusions were ranked in tier IC because of PD-1 and TRK tyrosine kinase inhibitors tissue-agnostic approvals. CDKN2A-inactivating alterations and EGFR amplification were ranked in tier IIA because of the efficacy of palbociclib (CDK4/6 inhibitor) and afatinib (tyrosine kinase inhibitor) in these respective molecular subgroups in retrospective analyses of clinical trials. Molecular alterations in several genes, including PIK3CA gene, were ranked in tier IIIA because of clinical benefit in other tumor types, whereas molecular alterations in IGF1R and TP53 genes were ranked in tier IVA and tier V, respectively. The most compelling actionable molecular alterations in HNSCC according to ESCAT include HRAS-activating mutations, MSI, high TMB, NTRK fusions, CDKN2A-inactivating alterations, and EGFR amplification.

Cost-effectiveness of precision cancer medicine-current challenges in the use of next generation sequencing for comprehensive tumour genomic profiling and the role of clinical utility frameworks (Review)

Precision cancer medicine (PCM) is an emerging paradigm in oncology, which includes tumour comprehensive genomic profiling (CGP) to enable molecularly guided therapy. However, cost-effectiveness analyses of PCM are faced with several challenges and, thus, its cost-effectiveness remains unclear. Early trials using only molecularly guided therapy were faced with the challenge of providing adequate measures of outcome, which probably explains the modest treatment benefits demonstrated. Endpoints like the progression-free survival (PFS)2/PFS1 ratio may assist in overcoming this issue. Moreover, specific tumour subtypes appear to benefit more from PCM. Costs associated with next-generation sequencing (NGS) for CGP are decreasing, but targeted therapy itself represents a major cost driver. CGP not only enables prediction of response to treatment, but also resistance, and could thus prevent administration of unnecessary (and costly) therapies. In clinical practice, the presence of clinical frameworks, such as the Recommendations for the Use of NGS for Patients with Metastatic Cancers from the ESMO Precision Medicine Working Group, and the ESMO Scale for Clinical Actionability of Molecular Targets, are essential in appropriately identifying situations where PCM is clinically meaningful, thereby improving its cost-effectiveness.

Amphipathic Small Molecule AZT Compound Displays Potent Inhibitory Effects in Cancer Cell Proliferation

Cancer has been identified as a leading cause of death worldwide, and the increasing number of cancer cases threatens to shorten the average life expectancy of people. Recently, we reported a 3-azido-3-deoxythymidine (AZT)-based amphipathic small molecule, ADG-2e that revealed a notable potency against tumor metastasis. To evaluate the anticancer potential of ADG-2e, we assessed its anticancer potency in vitro and in vivo. Anticancer screening of ADG-2e against cervical cancer cells, HeLa CCL2, and BT549 mammary gland ductal carcinoma showed significant inhibition of cancer cell proliferation. Furthermore, mechanistic investigations revealed that cancer cell death presumably proceeded through an oncosis mechanistic pathway because ADG-2e treated cells showed severe damage on the plasma membrane, a loss of membrane integrity, and leakage of α-tubulin and β-actin. Finally, evaluation of the antitumorigenic potential of ADG-2e in mouse xenograft models revealed that this compound potentially inhibits cancer cell proliferation. Collectively, these findings suggest that ADG-2e can evolve as an anticancer agent, which may represent a model for nucleoside-based small molecule anticancer drug discovery.

Piperine analogs arrest c-myc gene leading to downregulation of transcription for targeting cancer

G-quadruplex (G4) structures are considered a promising therapeutic target in cancer. Since Ayurveda, Piperine has been known for its medicinal properties. Piperine shows anticancer properties by stabilizing the G4 motif present upstream of the c-myc gene. This gene belongs to a group of proto-oncogenes, and its aberrant transcription drives tumorigenesis. The transcriptional regulation of the c-myc gene is an interesting approach for anticancer drug design. The present study employed a chemical similarity approach to identify Piperine similar compounds and analyzed their interaction with cancer-associated G-quadruplex motifs. Among all Piperine analogs, PIP-2 exhibited strong selectivity, specificity, and affinity towards c-myc G4 DNA as elaborated through biophysical studies such as fluorescence emission, isothermal calorimetry, and circular dichroism. Moreover, our biophysical observations are supported by molecular dynamics analysis and cellular-based studies. Our study showed that PIP-2 showed higher toxicity against the A549 lung cancer cell line but lower toxicity towards normal HEK 293 cells, indicating increased efficacy of the drug at the cellular level. Biological evaluation assays such as TFP reporter assay, quantitative real-time PCR (qRT- PCR), and western blotting suggest that the Piperine analog-2 (PIP-2) stabilizes the G-quadruplex motif located at the promoter site of c-myc oncogene and downregulates its expression. In conclusion, Piperine analog PIP-2 may be used as anticancer therapeutics as it affects the c-myc oncogene expression via G-quadruplex mediated mechanism.

Utility of comprehensive genomic profiling in directing treatment and improving patient outcomes in advanced non-small cell lung cancer

BACKGROUND

With the identification of new targetable drivers and the recent emergence of novel targeted drugs, using comprehensive genomic profiling in lieu of the routine testing for classic drivers in the clinical care for advanced NSCLC has been increasingly advocated. However, the key assumption justifying this practice, that comprehensive genomic profiling could lead to effective anticancer therapies and improve patient outcomes, remains unproved.

METHODS

Comprehensive genomic profiling was prospectively applied in 1564 advanced NSCLC patients to identify potentially actionable genomic alterations. Patients were assigned to genotype-matched targeted therapies or nonmatched therapies based on the profiling results. Its utility in directing treatments was determined by the proportion of patients receiving genotype-matched targeted therapies and the proportion of patients being enrolled into genotype-matched clinical trials. Its impacts on patient outcomes were assessed by comparing progression-free survival (PFS) and overall survival (OS) between patients who received a genotype-matched and nonmatched therapy.

RESULTS

From October 2016 to October 2019, tumor genomic profiles were established in 1166 patients, leading to a matched targeted therapy in 37.7% (n = 440) and a genotype-matched trial enrollment in 20.9% of patients (n = 244). Potentially actionable alterations were detected in 781 patients (67.0%). For these patients, a genomic profiling-directed matched therapy significantly improved PFS (9.0 months vs 4.9 months, P < 0.001) and OS (3.9 years vs 2.5 years, P < 0.001) compared with a nonmatched therapy. Excluding patients with standard targeted therapies, genomic profiling led to a matched targeted therapy in 16.7% (n = 24) and a matched trial enrollment in 11.2% (n = 16) of patients. No PFS (4.7 months vs 4.6 months, P = 0.530) or OS (1.9 years vs 2.4 years, P = 0.238) benefit was observed with the use of genotype-matched targeted therapies in this population.

CONCLUSIONS

Comprehensive genomic profiling is of clinical utility in assisting treatment selection, facilitating clinical trial enrollment, and improving patient outcomes in advanced NSCLC. However, for patients carrying alterations without standard-of-care targeted drugs, the interpretation of genomic profiling results should be careful given the low likelihood of benefit from the investigational or off-label use of targeted therapies in this population in the current treatment landscape.

TRIAL REGISTRATION

ChiCTR1900027582 (retrospectively registered on 19 November 2019).

Alternate dosage formulations of oral targeted anticancer agents

OBJECTIVE

Oral anticancer therapies have demonstrated superior outcomes compared to traditional cytotoxic chemotherapy in many disease states. However, certain patients may not be candidates for these agents due to odynphagia or dysphagia. The purpose of this review is to summarize the data for extemporaneous compounding of oral anticancer agents.

DATA SOURCES

Food and drug administration approvals of oncology agents were reviewed to identify oral anticancer therapies. In order to find alternative administration options: the package inserts of each of these agents were reviewed, each medication was searched in a tertiary drug information resource, the medical information teams of each drug manufacturer were contacted to inquire about proprietary data, sites with pediatric trials were contacted, a primary literature search was performed, and listservs for national pharmacy and oncology organizations were reviewed.

DATA SUMMARY

Eighty-five food and drug administration-approved oral anticancer therapies were identified to be included. Of those agents, nine (11%), had information in the package insert related to alternative administration. After further research, 46 (54%) of the agents had some information related to alternate formulations for administration. The recipes and stability of each of these compounds is included.

CONCLUSIONS

The majority of agents do not have Phase I or II trials that assess safety or pharmacokinetics of alternative formulations. Clinicians should exercise caution when recommending or administering these agents outside of food and drug administration-approved indicated use and utilize clinical judgment in assessing the risks and benefits of altering anticancer compounds. However, the alternative administration considerations presented can increase access to oncology patients who have difficulty swallowing.