Evolution of precision oncology-guided treatment paradigms

Cancer treatment is gradually evolving from the classical use of nonspecific cytotoxic drugs targeting generic mechanisms of cell growth and proliferation. Instead, new "patient-specific treatment paradigms" that are based on an individual patient's tumor-specific molecular features are emerging, and these include "druggable" genomic alterations such as oncogenic driver mutations, downstream activities of cancer-signaling pathways, and the expression of specific genes involved in tumorigenesis and cancer progression. This evolving landscape of making evidence-based treatment decisions forms the foundation of precision oncology, which aims to deliver "the right drug, to the right patient and at the right time". The long-term vision for this approach is to maximize the treatment efficacy while minimizing exposure to ineffective therapy and reducing co-morbidity-related side effects. Successful clinical translation and implementation of this vision have the potential to revolutionize treatment paradigms from predominantly reactive, to more evidence-based, proactive and predictive care. In this article, we review the past and current approaches in precision oncology, and describe their remarkable power and limitations. We also speculate on the evolution of newly emerging methodologies of the future that can be used to address some of the key challenges associated with the existing paradigms. This article is categorized under: Cancer > Genetics/Genomics/Epigenetics Cancer > Molecular and Cellular Physiology Cancer > Computational Models.

Targeted treatment of solid tumors in pediatric precision oncology

The treatment of childhood solid cancer has markedly evolved in recent years following a refined molecular characterization and the introduction of novel targeted drugs. On one hand, larger sequencing studies have revealed a spectrum of mutations in pediatric tumors different from adults. On the other hand, specific mutations or immune dysregulated pathways have been targeted in preclinical and clinical studies, with heterogeneous results. Of note, the development of national platforms for tumor molecular profiling and, in less measure, for targeted treatment, has been essential in the process. However, many of the available molecules have been tested only in relapsed or refractory patients, and have proven poorly effective, at least in monotherapy. Our future approaches should certainly aim at improving the access to molecular characterization, to obtain a deeper picture of the distinctive phenotype of childhood cancer. In parallel, the implementation of access to novel drugs should not only be limited to basket or umbrella studies but also to larger, multi-drug international studies. In this paper we reviewed the molecular features and the main available therapeutic options in pediatric solid cancer, focusing on available targeted drugs and ongoing investigations, aiming at providing a useful tool to navigate the heterogeneity of this promising but complex field.

RAPID framework for improved access to precision oncology for lethal disease: Results from a modified multi-round delphi study

Introduction

Predictive oncology, germline technologies, and adaptive seamless trials are promising advances in the treatment of lethal cancers. Yet, access to these therapies is stymied by costly research, regulatory barriers, and structural inequalities worsened by the COVID-19 pandemic.

Methods

To address the need for a comprehensive strategy for rapid and more equitable access to breakthrough therapies for lethal cancers, we conducted a modified multi-round Delphi study with 70 experts in oncology, clinical trials, legal and regulatory processes, patient advocacy, ethics, drug development, and health policy in Canada, Europe, and the US. Semi-structured ethnographic interviews (n = 33) were used to identify issues and solutions that participants subsequently evaluated in a survey (n = 47). Survey and interview data were co-analyzed to refine topics for an in-person roundtable where recommendations for system change were deliberated and drafted by 26 participants.

Results

Participants emphasized major issues in patient access to novel therapeutics including burdens of time, cost, and transportation required to complete eligibility requirements or to participate in trials. Only 12% of respondents reported satisfaction with current research systems, with "patient access to trials" and "delays in study approval" the topmost concerns.

Conclusion

Experts agree that an equity-centered precision oncology communication model should be developed to improve access to adaptive seamless trials, eligibility reforms, and just-in-time trial activation. International advocacy groups are a key mobilizer of patient trust and should be involved at every stage of research and therapy approval. Our results also show that governments can promote better and faster access to life-saving therapeutics by engaging researchers and payors in an ecosystem approach that responds to the unique clinical, structural, temporal, and risk-benefit situations that patients with life-threatening cancers confront.

Efficacy of immunotherapy in oncogene-driven non-small-cell lung cancer

For advanced metastatic non-small-lung cancer, the landscape of actionable driver alterations is rapidly growing, with nine targetable oncogenes and seven approvals within the last 5 years. This accelerated drug development has expanded the reach of targeted therapies, and it may soon be that a majority of patients with lung adenocarcinoma will be eligible for a targeted therapy during their treatment course. With these emerging therapeutic options, it is important to understand the existing data on immune checkpoint inhibitors (ICIs), along with their efficacy and safety for each oncogene-driven lung cancer, to best guide the selection and sequencing of various therapeutic options. This article reviews the clinical data on ICIs for each of the driver oncogene defined lung cancer subtypes, including efficacy, both for ICI as monotherapy or in combination with chemotherapy or radiation; toxicities from ICI/targeted therapy in combination or in sequence; and potential strategies to enhance ICI efficacy in oncogene-driven non-small-cell lung cancers.

Interactive process mining of cancer treatment sequences with melanoma real-world data

The growing availability of clinical real-world data (RWD) represents a formidable opportunity to complement evidence from randomized clinical trials and observe how oncological treatments perform in real-life conditions. In particular, RWD can provide insights on questions for which no clinical trials exist, such as comparing outcomes from different sequences of treatments. To this end, process mining is a particularly suitable methodology for analyzing different treatment paths and their associated outcomes. Here, we describe an implementation of process mining algorithms directly within our hospital information system with an interactive application that allows oncologists to compare sequences of treatments in terms of overall survival, progression-free survival and best overall response. As an application example, we first performed a RWD descriptive analysis of 303 patients with advanced melanoma and reproduced findings observed in two notorious clinical trials: CheckMate-067 and DREAMseq. Then, we explored the outcomes of an immune-checkpoint inhibitor rechallenge after a first progression on immunotherapy versus switching to a BRAF targeted treatment. By using interactive process-oriented RWD analysis, we observed that patients still derive long-term survival benefits from immune-checkpoint inhibitors rechallenge, which could have direct implications on treatment guidelines for patients able to carry on immune-checkpoint therapy, if confirmed by external RWD and randomized clinical trials. Overall, our results highlight how an interactive implementation of process mining can lead to clinically relevant insights from RWD with a framework that can be ported to other centers or networks of centers.

Clinical management of molecular alterations identified by high throughput sequencing in patients with advanced solid tumors in treatment failure: Real-world data from a French hospital

Background

In the context of personalized medicine, screening patients to identify targetable molecular alterations is essential for therapeutic decisions such as inclusion in clinical trials, early access to therapies, or compassionate treatment. The objective of this study was to determine the real-world impact of routine incorporation of FoundationOne analysis in cancers with a poor prognosis and limited treatment options, or in those progressing after at least one course of standard therapy.

Methods

A FoundationOneCDx panel for solid tumor or liquid biopsy samples was offered to 204 eligible patients.

Results

Samples from 150 patients were processed for genomic testing, with a data acquisition success rate of 93%. The analysis identified 2419 gene alterations, with a median of 11 alterations per tumor (range, 0-86). The most common or likely pathogenic variants were on TP53, TERT, PI3KCA, CDKN2A/B, KRAS, CCDN1, FGF19, FGF3, and SMAD4. The median tumor mutation burden was three mutations/Mb (range, 0-117) in 143 patients with available data. Of 150 patients with known or likely pathogenic actionable alterations, 13 (8.6%) received matched targeted therapy. Sixty-nine patients underwent Molecular Tumor Board, which resulted in recommendations in 60 cases. Treatment with genotype-directed therapy had no impact on overall survival (13 months vs. 14 months; p = 0.95; hazard ratio = 1.04 (95% confidence interval, 0.48-2.26)].

Conclusions

This study highlights that an organized center with a Multidisciplinary Molecular Tumor Board and an NGS screening system can obtain satisfactory results comparable with those of large centers for including patients in clinical trials.

Case report: Complete and durable response to larotrectinib (TRK inhibitor) in an infant diagnosed with angiosarcoma harbouring a KHDRBS1-NTRK3 fusion gene

Significant improvements in the survival rates of paediatric cancer have been achieved over the past decade owing to recent advances in therapeutic and diagnostic strategies. However, disease progression and relapse remain a major challenge for the clinical management of paediatric angiosarcoma. Comprehensive genomic profiling of these rare tumours using high-throughput sequencing technologies may improve patient stratification and identify actionable biomarkers for therapeutic intervention. Here, we describe the clinical, histopathological, immunohistochemical and molecular profile of a novel and precision medicine-informed case where a KHDRBS1-NTRK3 fusion determined by next-generation sequencing-based comprehensive genomic profiling led to complete and sustained remission (clinical and radiological response) in an otherwise incurable disease. Our patient represents the first paediatric angiosarcoma harbouring a targetable NTRK3 fusion in the literature and demonstrates the first example of targeting this alteration in angiosarcoma using larotrectinib, an NTRK inhibitor. Clinical and radiological remission was achieved in under two months of therapy, and the patient is currently in complete remission, 4 month after stopping larotrectinib therapy, which was given over 17 months with only mild side effects reported. Therefore, this remarkable case exemplifies the true essence of precision-based care by incorporating conventional pathology with the why, when, and how to test for rare oncogenic drivers and agnostic biomarkers in paediatric angiosarcoma.

Frequency of Pathogenic Germline Mutations in Early and Late Onset Familial Breast Cancer Patients Using Multi-Gene Panel Sequencing: An Egyptian Study

BACKGROUND

Precision oncology has been increasingly used in clinical practice and rapidly evolving in the oncology field. Thus, this study was performed to assess the frequency of germline mutations in early and late onset familial breast cancer (BC) Egyptian patients using multi-gene panel sequencing to better understand the contribution of the inherited germline mutations in BC predisposition. Moreover, to determine the actionable deleterious mutations associated with familial BC that might be used as biomarker for early cancer detection.

METHODS

Whole blood samples were collected from 101 Egyptian patients selected for BC family history, in addition to 50 age-matched healthy controls. A QIAseq targeted DNA panel (human BC panel) was used to assess the frequency of germline mutations.

RESULTS

A total of 58 patients (57.4%) out of 101 were found to have 27 deleterious germline mutations in 11 cancer susceptibility genes. Of them, 32 (31.6%) patients carried more than one pathogenic mutation and each one carried at least one pathogenic mutation. The major genes harboring the pathogenic mutations were: ATM, BRCA2, BRCA1, VHL, MSH6, APC, CHEK2, MSH2, MEN1, PALB2, and MUTYH. Thirty-one patients (30.6%) had BRCA2 mutations and twenty (19.8%) had BRCA1 mutations. Our results showed that exon 10 and exon 11 harbored 3 and 5 mutations, respectively, in BRCA1 and BRCA2 genes. Our analysis also revealed that the VHL gene significantly co-occurred with each of the BRCA2 gene (p = 0.003, event ratio 11/21), the MSH2 gene (p = 0.01, 4/10), the CHEK2 gene (p = 0.02, 4/11), and the MSH6 gene (p = 0.04, 4/12). In addition, the APC gene significantly co-occurred with the MSH2 gene (p = 0.01, 3/7). Furthermore, there was a significant mutually exclusive event between the APC gene and the ATM gene (p = 0.04, 1/36). Interestingly, we identified population specific germline mutations in genes showing potentials for targeted therapy to meet the need for incorporating precision oncology into clinical practice. For example, the mutations identified in the ATM, APC, and MSH2 genes.

CONCLUSIONS

Multi-gene panel sequencing was used to detect the deleterious mutations associated with familial BC, which in turns mitigate the essential need for implementing next generation sequencing technologies in precision oncology to identify cancer predisposing genes. Moreover, identifying DNA repair gene mutations, with focus on non-BRCA genes, might serve as candidates for targeted therapy and will be increasingly used in precision oncology.

A molecular classification of gastric cancer associated with distinct clinical outcomes and validated by an XGBoost-based prediction model

Gastric cancer (GC) is a heterogeneous disease and a leading cause of cancer-related deaths. Discovering robust, clinically relevant molecular classifications is critical for guiding personalized therapies for GC. Here, we propose a refined molecular classification scheme for GC using integrated optimal algorithms and multi-omics data. Based on the important features of mRNA, microRNA, and DNA methylation data selected by the multivariate Cox regression model, three subtypes linked to distinct clinical outcomes were identified by combining similarity network fusion and consensus clustering methods. Three subtypes were validated by an extreme gradient boosting machine learning prediction model with 125 differentially expressed genes in multiple independent cohorts. The molecular characteristics of mutation signatures, characteristic gene sets, driver genes, and chemotherapy sensitivity for each subtype were also identified: subtype 1 was associated with favorable prognosis and characterized by high ARID1A and PIK3CA mutations, subtype 2 was associated with a poor prognosis and harbored high recurrent TP53 mutations, and subtype 3 was associated with high CHD1, APOA1 mutations, and a poor prognosis. The proposed three-subtype scheme achieved a better clinical prediction performance (area under the curve value = 0.71) than The Cancer Genome Atlas classification, which may provide a practical subtyping framework to improve the treatment of GC.

DNA Damage Repair Defects and Targeted Radionuclide Therapies for Prostate Cancer: Does Mutation Really Matter? A Systematic Review

The aim of the present review was to assess the impact of DNA damage repair (DDR) mutations on response and outcome of patients (pts) affected by advanced prostate cancer (PCa) submitted to radionuclide therapies with [223Ra]RaCl2 (223Ra-therapy) or prostate specific membrane antigen (PSMA) ligands. A systematic literature search according to PRISMA criteria was made by using two main databases. Only studies published up until to October 2022 in the English language with ≥10 enrolled patients were selected. Seven studies including 326 pts, of whom 201 (61.6%) harboring DDR defects, were selected. The majority of selected papers were retrospective and four out of seven (57.1%) had small sample size (<50 pts). Three out of seven (42.8%) studies reported a more favorable outcome (overall or progression free survival) after therapy with alpha emitters (223Ra-therapy or [225Ac]Ac-PSMA-617) in subjects with DDR defects with respect to those without mutations. In two studies employing alpha or beta emitters ([177Lu]/[225Ac]-PMSA), no significant benefit was registered in pts harboring DDR defects. In all but one paper, no significant difference in response rate was reported among pts with or without DDR mutations. Although preliminary and biased by the retrospective design, preliminary data suggest a trend towards a longer survival in PCa pts harboring DDR defects submitted to radionuclide targeted therapy with alpha emitters.

Multi-analyte liquid biopsies for molecular pathway guided personalized treatment selection in advanced refractory cancers: A clinical utility pilot study

Purpose

The selection of safe and efficacious anticancer regimens for treatment of patients with broadly refractory metastatic cancers remains a clinical challenge. Such patients are often fatigued by toxicities of prior failed treatments and may have no further viable standard of care treatment options. Liquid Biopsy-based multi-analyte profiling in peripheral blood can identify a majority of drug targets that can guide the selection of efficacious combination regimens.

Patients and methods

LIQUID IMPACT was a pilot clinical study where patients with advanced refractory cancers received combination anticancer treatment regimens based on multi-analyte liquid biopsy (MLB) profiling of circulating tumor biomarkers; this study design was based on the findings of prior feasibility analysis to determine the abundance of targetable variants in blood specimens from 1299 real-world cases of advanced refractory cancers.

Results

Among the 29 patients in the intent to treat (ITT) cohort of the trial, 26 were finally evaluable as per study criteria out of whom 12 patients showed Partial Response (PR) indicating an Objective Response Rate (ORR) of 46.2% and 11 patients showed Stable Disease (SD) indicating the Disease Control Rate (DCR) to be 88.5%. The median Progression-Free Survival (mPFS) and median Overall Survival (mOS) were 4.3 months (95% CI: 3.0 - 5.6 months) and 8.8 months (95% CI: 7.0 - 10.7 months), respectively. Toxicities were manageable and there were no treatment-related deaths.

Conclusion

The study findings suggest that MLB could be used to assist treatment selection in heavily pretreated patients with advanced refractory cancers.

Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers

Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.

A Comparison of Patients' and Physicians' Knowledge and Expectations Regarding Precision Oncology Tests

(1) Background: As genomic testing is becoming a part of the mainstream oncology practice, it is vital to ensure that our patients fully understand the implications of these tests. This study aimed to compare the attitudes and expectations of cancer patients with those of their physicians regarding the role of biomarker testing in clinical decision making. (2) Methods: Two separate, complimentary, self-administered questionnaires for patients with cancer and their physicians, respectively, were collected in Calgary, Alberta, Canada. Out of 117, 113 completed patient surveys were included in the statistical analysis, constituting a 96.4% response rate. These surveys were subsequently matched with those of their corresponding oncologists to determine the concordance rates. (3) Results: Overall, patients demonstrated a good understanding of general cancer biology (80.0%) and diagnostic processes (90.0%) associated with precision oncology. Most patients wanted their tumours to be tested to guide treatment, and the oncologists broadly shared these views (concordance 65.1%). However, there were discrepancies between the knowledge and expectations regarding the applications of test results on actual diagnosis and prognosis between patients and their oncologists (concordance 26.1% and 36.0%, respectively). While only 28.0% of patients thought they had enough knowledge to make informed decisions, the majority (68.0%) said they needed more information. (4) Conclusion: Our study shows that patients and cancer physicians do not always agree with the roles and applications of genomic tests, which could lead to misplaced expectations and poor health outcomes. More research is needed to devise strategies to improve education and communication to align these expectations and improve the quality of clinical decision making.

Deep learning-based multi-drug synergy prediction model for individually tailored anti-cancer therapies

While synergistic drug combinations are more effective at fighting tumors with complex pathophysiology, preference compensating mechanisms, and drug resistance, the identification of novel synergistic drug combinations, especially complex higher-order combinations, remains challenging due to the size of combination space. Even though certain computational methods have been used to identify synergistic drug combinations in lieu of traditional in vitro and in vivo screening tests, the majority of previously published work has focused on predicting synergistic drug pairs for specific types of cancer and paid little attention to the sophisticated high-order combinations. The main objective of this study is to develop a deep learning-based approach that integrated multi-omics data to predict novel synergistic multi-drug combinations (DeepMDS) in a given cell line. To develop this approach, we firstly created a dataset comprising of gene expression profiles of cancer cell lines, target information of anti-cancer drugs, and drug response against a large variety of cancer cell lines. Based on the principle of a fully connected feed forward Deep Neural Network, the proposed model was constructed using this dataset, which achieved a high performance with a Mean Square Error (MSE) of 2.50 and a Root Mean Squared Error (RMSE) of 1.58 in the regression task, and gave the best classification accuracy of 0.94, an area under the Receiver Operating Characteristic curve (AUC) of 0.97, a sensitivity of 0.95, and a specificity of 0.93. Furthermore, we utilized three breast cancer cell subtypes (MCF-7, MDA-MD-468 and MDA-MB-231) and one lung cancer cell line A549 to validate the predicted results of our model, showing that the predicted top-ranked multi-drug combinations had superior anti-cancer effects to other combinations, particularly those that were widely used in clinical treatment. Our model has the potential to increase the practicality of expanding the drug combinational space and to leverage its capacity to prioritize the most effective multi-drug combinational therapy for precision oncology applications.

The prospect of tumor microenvironment-modulating therapeutical strategies

Multiple mechanisms promote tumor prosperity, which does not only depend on cell-autonomous, inherent abnormal characteristics of the malignant cells that facilitate rapid cell division and tumor expansion. The neoplastic tissue is embedded in a supportive and dynamic tumor microenvironment (TME) that nurtures and protects the malignant cells, maintaining and perpetuating malignant cell expansion. The TME consists of different elements, such as atypical vasculature, various innate and adaptive immune cells with immunosuppressive or pro-inflammatory properties, altered extracellular matrix (ECM), activated stromal cells, and a wide range of secreted/stroma-tethered bioactive molecules that contribute to malignancy, directly or indirectly. In this review, we describe the various TME components and provide examples of anti-cancer therapies and novel drugs under development that aim to target these components rather than the intrinsic processes within the malignant cells. Combinatory TME-modulating therapeutic strategies may be required to overcome the resistance to current treatment options and prevent tumor recurrence.

Therapeutic Targeting of DNA Damage Repair in the Era of Precision Oncology and Immune Checkpoint Inhibitors

Cancer manifestation is a multistep process involving accumulation of various genetic and epigenetic changes that results in oncogenic "hallmarks of cancer" processes including genomic instability. Exploitation of aberrant DNA-damage response (DDR) mechanisms in cancer is in part a goal of many therapeutic strategies, and recent evidence supports the role of targeting DDR in modulating the tumor immune microenvironment to enhance immunotherapeutic response. Improved cancer profiling, including next-generation and whole-genome mutational sequencing of tumor tissue, as well as circulating nucleic acids, has enhanced our understanding of the genetic and epigenetic molecular mechanisms in tumorigenesis and will become fundamental to precisely target tumors and achieve cancer control. With the successes of poly(ADP-ribose) polymerase inhibitors (PARPi) and immunotherapies, the intersection of DDR molecular machinery and corresponding antitumor immune response has gained much interest with a focus on achieving therapeutic synergy using DNA damage-targeting agents and immunotherapy. In this review, we provide a bench-to-bedside overview of the fundamentals of DDR signaling and repair as they relate to cancer therapeutic strategies including novel DDR-targeting agents. We also discuss the underlying mechanisms that link DDR signaling to antitumor immunity and immunotherapy efficacy, and how this knowledge can be used to improve precision medicine approaches in the treatment of cancer.

EGFR, the Lazarus target for precision oncology in glioblastoma

The Lazarus effect is a rare condition that happens when someone seemingly dead shows signs of life. The epidermal growth factor receptor (EGFR) represents a target in the fatal neoplasm glioblastoma (GBM) that through a series of negative clinical trials has prompted a vocal subset of the neuro-oncology community to declare this target dead. However, an argument can be made that the core tenets of precision oncology were overlooked in the initial clinical enthusiasm over EGFR as a therapeutic target in GBM. Namely, the wrong drugs were tested on the wrong patients at the wrong time. Furthermore, new insights into the biology of EGFR in GBM vis-à-vis other EGFR-driven neoplasms, such as non-small cell lung cancer, and development of novel GBM-specific EGFR therapeutics resurrects this target for future studies. Here, we will examine the distinct EGFR biology in GBM, how it exacerbates the challenge of treating a CNS neoplasm, how these unique challenges have influenced past and present EGFR-targeted therapeutic design and clinical trials, and what adjustments are needed to therapeutically exploit EGFR in this devastating disease.

Evolution of Treatment in Advanced Cholangiocarcinoma: Old and New towards Precision Oncology

Cholangiocarcinoma (CCA) is a malignant neoplasm arising in the epithelium of the biliary tract. It represents the second most common primary liver cancer in the world, after hepatocellular carcinoma, and it constitutes 10-15% of hepatobiliary neoplasms and 3% of all gastrointestinal tumors. As in other types of cancers, recent studies have revealed genetic alterations underlying the establishment and progression of CCA. The most frequently involved genes are APC, ARID1A, AXIN1, BAP1, EGFR, FGFRs, IDH1/2, RAS, SMAD4, and TP53. Actionable targets include alterations of FGFRs, IDH1/2, BRAF, NTRK, and HER2. "Precision oncology" is emerging as a promising approach for CCA, and it is possible to inhibit the altered function of these genes with molecularly oriented drugs (pemigatinib, ivosidenib, vemurafenib, larotrectinib, and trastuzumab). In this review, we provide an overview of new biologic drugs (their structures, mechanisms of action, and toxicities) to treat metastatic CCA, providing readers with panoramic information on the trajectory from "old" chemotherapies to "new" target-oriented drugs.

Salivary gland cancers in elderly patients: challenges and therapeutic strategies

Salivary gland carcinomas (SGCs) are the most heterogeneous subgroup of head and neck malignant tumors, accounting for more than 20 subtypes. The median age of SGC diagnosis is expected to rise in the following decades, leading to crucial clinical challenges in geriatric oncology. Elderly patients, in comparison with patients aged below 65 years, are generally considered less amenable to receiving state-of-the-art curative treatments for localized disease, such as surgery and radiation/particle therapy. In the advanced setting, chemotherapy regimens are often dampened by the consideration of cardiovascular and renal comorbidities. Nevertheless, the elderly population encompasses a broad spectrum of functionalities. In the last decades, some screening tools (e.g. the G8 questionnaire) have been developed to identify those subjects who should receive a multidimensional geriatric assessment, to answer the question about the feasibility of complex treatments. In the present article, we discuss the most frequent SGC histologies diagnosed in the elderly population and the relative 5-years survival outcomes based on the most recent data from the Surveillance, Epidemiology, and End Results (SEER) Program. Moreover, we review the therapeutic strategies currently available for locoregionally advanced and metastatic disease, taking into account the recent advances in precision oncology. The synergy between the Multidisciplinary Tumor Board and the Geriatrician aims to shape the most appropriate treatment pathway for each elderly patient, focusing on global functionality instead of the sole chronological age.

Opportunities and challenges in interpretable deep learning for drug sensitivity prediction of cancer cells

In precision oncology, therapy stratification is done based on the patients' tumor molecular profile. Modeling and prediction of the drug response for a given tumor molecular type will further improve therapeutic decision-making for cancer patients. Indeed, deep learning methods hold great potential for drug sensitivity prediction, but a major problem is that these models are black box algorithms and do not clarify the mechanisms of action. This puts a limitation on their clinical implementation. To address this concern, many recent studies attempt to overcome these issues by developing interpretable deep learning methods that facilitate the understanding of the logic behind the drug response prediction. In this review, we discuss strengths and limitations of recent approaches, and suggest future directions that could guide further improvement of interpretable deep learning in drug sensitivity prediction in cancer research.

Are targeted therapies or immunotherapies effective in metastatic pancreatic adenocarcinoma?

Metastatic pancreatic ductal adenocarcinoma (PDAC) is a major health burden due to its increasing incidence and poor prognosis. PDAC is characterized by a low tumor mutational burden, and its molecular pathogenesis is driven by Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations. Response to DNA damage through homologous repair is defective in 15% of tumors. Chemotherapy using FOLFIRINOX (folinic acid, fluorouracil, irinotecan, oxaliplatin) or gemcitabine-nab-paclitaxel significantly improves life expectancy, but the median overall survival remains <1 year. Targeted therapies are not efficient in the overall population of patients with metastatic PDAC. Improvements in overall survival or progression-free survival, however, have been demonstrated in subgroups carrying certain mutations. Maintenance therapy with poly-ADP-ribose polymerase (PARP) inhibitors increases progression-free survival in patients with germline mutations in BRCA1/2. Sotorasib shows signs of efficacy against tumors carrying the KRAS G12C mutation, and targeted therapies may also benefit patients with KRAS-wild-type PDAC. Combining targeted therapies with chemotherapy holds promise because of potential synergistic effects. These associations, however, have not yet demonstrated clinical benefit. Checkpoint inhibitors are not effective against metastatic PDAC. Combined immunotherapies attempt to restore their efficacy but have not succeeded yet. Other immunotherapies are emerging such as therapeutic vaccines or chimeric antigen receptor (CAR) T cells, but these strategies remain to be evaluated in large trials. In the future, treatment personalization based on tumor-derived organoids could potentially further improve treatment efficiency.

Methylation of CpG Sites as Biomarkers Predictive of Drug-Specific Patient Survival in Cancer

Background:

Though the development of targeted cancer drugs continues to accelerate, doctors still lack reliable methods for predicting patient response to standard-of-care therapies for most cancers. DNA methylation has been implicated in tumor drug response and is a promising source of predictive biomarkers of drug efficacy, yet the relationship between drug efficacy and DNA methylation remains largely unexplored.

Method:

In this analysis, we performed log-rank survival analyses on patients grouped by cancer and drug exposure to find CpG sites where binary methylation status is associated with differential survival in patients treated with a specific drug but not in patients with the same cancer who were not exposed to that drug. We also clustered these drug-specific CpG sites based on co-methylation among patients to identify broader methylation patterns that may be related to drug efficacy, which we investigated for transcription factor binding site enrichment using gene set enrichment analysis.

Results:

We identified CpG sites that were drug-specific predictors of survival in 38 cancer-drug patient groups across 15 cancers and 20 drugs. These included 11 CpG sites with similar drug-specific survival effects in multiple cancers. We also identified 76 clusters of CpG sites with stronger associations with patient drug response, many of which contained CpG sites in gene promoters containing transcription factor binding sites.

Conclusion:

These findings are promising biomarkers of drug response for a variety of drugs and contribute to our understanding of drug-methylation interactions in cancer. Investigation and validation of these results could lead to the development of targeted co-therapies aimed at manipulating methylation in order to improve efficacy of commonly used therapies and could improve patient survival and quality of life by furthering the effort toward drug response prediction.