Interpreting and integrating genomic tests results in clinical cancer care: Overview and practical guidance.

The last decade has seen rapid progress in the use of genomic tests, including gene panels, whole-exome sequencing, and whole-genome sequencing, in research and clinical cancer care. These advances have created expansive opportunities to characterize the molecular attributes of cancer, revealing a subset of cancer-associated aberrations called driver mutations. The identification of these driver mutations can unearth vulnerabilities of cancer cells to targeted therapeutics, which has led to the development and approval of novel diagnostics and personalized interventions in various malignancies. The applications of this modern approach, often referred to as precision oncology or precision cancer medicine, are already becoming a staple in cancer care and will expand exponentially over the coming years. Although genomic tests can lead to better outcomes by informing cancer risk, prognosis, and therapeutic selection, they remain underutilized in routine cancer care. A contributing factor is a lack of understanding of their clinical utility and the difficulty of results interpretation by the broad oncology community. Practical guidelines on how to interpret and integrate genomic information in the clinical setting, addressed to clinicians without expertise in cancer genomics, are currently limited. Building upon the genomic foundations of cancer and the concept of precision oncology, the authors have developed practical guidance to aid the interpretation of genomic test results that help inform clinical decision making for patients with cancer. They also discuss the challenges that prevent the wider implementation of precision oncology.

Machine learning-based reclassification of germline variants of unknown significance: The RENOVO algorithm.

The increasing scope of genetic testing allowed by next-generation sequencing (NGS) dramatically increased the number of genetic variants to be interpreted as pathogenic or benign for adequate patient management. Still, the interpretation process often fails to deliver a clear classification, resulting in either variants of unknown significance (VUSs) or variants with conflicting interpretation of pathogenicity (CIP); these represent a major clinical problem because they do not provide useful information for decision-making, causing a large fraction of genetically determined disease to remain undertreated. We developed a machine learning (random forest)-based tool, RENOVO, that classifies variants as pathogenic or benign on the basis of publicly available information and provides a pathogenicity likelihood score (PLS). Using the same feature classes recommended by guidelines, we trained RENOVO on established pathogenic/benign variants in ClinVar (training set accuracy = 99%) and tested its performance on variants whose interpretation has changed over time (test set accuracy = 95%). We further validated the algorithm on additional datasets including unreported variants validated either through expert consensus (ENIGMA) or laboratory-based functional techniques (on BRCA1/2 and SCN5A). On all datasets, RENOVO outperformed existing automated interpretation tools. On the basis of the above validation metrics, we assigned a defined PLS to all existing ClinVar VUSs, proposing a reclassification for 67% with >90% estimated precision. RENOVO provides a validated tool to reduce the fraction of uninterpreted or misinterpreted variants, tackling an area of unmet need in modern clinical genetics.

Evaluation of the Geographical Accessibility of Genome-Matched Clinical Trials on a National Experience.

BACKGROUND: Molecular-driven oncology allows oncologists to identify treatments that match a cancer's genomic profile. Clinical trials are promoted as an effective modality to deliver a molecularly matched treatment. We explore the role of geographical accessibility in Italy, and its impact on patient access to clinical trials. MATERIAL AND METHODS: We retrospectively reviewed molecular data from a single-institutional case series of patients receiving next-generation sequencing testing between March 2019 and July 2020. Actionable alterations were defined as the ones with at least one matched treatment on Clinicaltrials.gov at the time of genomic report signature. We then calculated the hypothetical distance to travel to reach the nearest assigned clinical trial. RESULTS: We identified 159 patients eligible for analysis. One hundred and one could be potentially assigned to a clinical trial in Italy, and the median distance that patients needed to travel to reach the closest location with a suitable clinical trial was 76 km (interquartile range = 127.46 km). Geographical distribution of clinical trials in Italy found to be heterogeneous, with Milan and Naples being the areas with a higher concentration. We then found that the probability of having a clinical trial close to a patient's hometown increased over time, according to registered studies between 2015 and 2020. CONCLUSIONS: The median distance to be travelled to the nearest trial was generally acceptable for patients, and trials availability is increasing. Nevertheless, many areas are still lacking trials, so efforts are required to increase and homogenize the possibilities to be enrolled in clinical trials for Italian patients with cancer.

Immune-Related Adverse Event Likelihood Score Identifies "Pure" IRAEs Strongly Associated With Outcome in a Phase I-II Trial Population.

BACKGROUND: Immune-related adverse events (IRAE) pose a significant diagnostic and therapeutic challenge in patients treated with immune-oncology (IO) drugs. IRAEs have been suggested to correlate with better outcome, but studies are conflicting. Estimating the true incidence of IRAEs is particularly difficult in the early phase I/II trial setting. A key issue is the lack of IRAE diagnostic criteria, necessary to discriminate "pure" IRAEs from other treatment-related adverse events not sustained by an autoimmune process. METHODS: In patients treated with immune-oncology (IO) drugs in phases I-II trials at our institute, we identified high confidence (HC) or low confidence (LC) IRAEs by clinical consensus. We empirically developed an IRAE likelihood score (ILS) based on commonly available clinical data. Correlation with outcome was explored by multivariate Cox analysis. To mitigate immortal time-bias, analyses were conducted (1) at 2-month landmark and (2) modeling IRAEs as time-dependent covariate. RESULTS: Among 202 IO-treated patients, 29.2% developed >1 treatment-related adverse events (TRAE). Based on ILS >5, we classified patients in no IRAE (n = 143), HC IRAE (n = 24), or LC IRAE (n = 35). hazard ratios (HR) for HC were significantly lower than LC patients (HR for PFS ranging 0.24-0.44, for OS 0.18-0.23, all P < .01). CONCLUSION: ILS provides a simple system to identify bona fide IRAEs, pruning for other treatment-related events likely due to different pathophysiology. Applying stringent criteria leads to lower and more reliable estimates of IRAE incidence and identifies events with significant impact on survival.

Benchmarking and improving the performance of variant-calling pipelines with RecallME.

MOTIVATION: The steady increment of Whole Genome/Exome sequencing and the development of novel Next Generation Sequencing-based gene panels requires continuous testing and validation of variant calling (VC) pipelines and the detection of sequencing-related issues to be maintained up-to-date and feasible for the clinical settings. State of the art tools are reliable when used to compute standard performance metrics. However, the need for an automated software to discriminate between bioinformatic and sequencing issues and to optimize VC parameters remains unmet. RESULTS: The aim of the current work is to present RecallME, a bioinformatic suite that tracks down difficult-to-detect variants as insertions and deletions in highly repetitive regions, thus providing the maximum reachable recall for both single nucleotide variants and small insertion and deletions and to precisely guide the user in the pipeline optimization process. AVAILABILITY AND IMPLEMENTATION: Source code is freely available under MIT license at https://github.com/mazzalab-ieo/recallme. RecallME web application is available at https://translational-oncology-lab.shinyapps.io/recallme/. To use RecallME, users must obtain a license for ANNOVAR by themselves.

Comprehensive genomic profiling on metastatic Melanoma: results from a network screening from 7 Italian Cancer Centres.

BACKGROUND: The current therapeutic algorithm for Advanced Stage Melanoma comprises of alternating lines of Targeted and Immuno-therapy, mostly via Immune-Checkpoint blockade. While Comprehensive Genomic Profiling of solid tumours has been approved as a companion diagnostic, still no approved predictive biomarkers are available for Melanoma aside from BRAF mutations and the controversial Tumor Mutational Burden. This study presents the results of a Multi-Centre Observational Clinical Trial of Comprehensive Genomic Profiling on Target and Immuno-therapy treated advanced Melanoma. METHODS: 82 samples, collected from 7 Italian Cancer Centres of FFPE-archived Metastatic Melanoma and matched blood were sequenced via a custom-made 184-gene amplicon-based NGS panel. Sequencing and bioinformatics analysis was performed at a central hub. Primary analysis was carried out via the Ion Reporter framework. Secondary analysis and Machine Learning modelling comprising of uni and multivariate, COX/Lasso combination, and Random Forest, was implemented via custom R/Python scripting. RESULTS: The genomics landscape of the ACC-mela cohort is comparable at the somatic level for Single Nucleotide Variants and INDELs aside a few gene targets. All the clinically relevant targets such as BRAF and NRAS have a comparable distribution thus suggesting the value of larger scale sequencing in melanoma. No comparability is reached at the CNV level due to biotechnological biases and cohort numerosity. Tumour Mutational Burden is slightly higher in median for Complete Responders but fails to achieve statistical significance in Kaplan-Meier survival analysis via several thresholding strategies. Mutations on PDGFRB, NOTCH3 and RET were shown to have a positive effect on Immune-checkpoint treatment Overall and Disease-Free Survival, while variants in NOTCH4 were found to be detrimental for both endpoints. CONCLUSIONS: The results presented in this study show the value and the challenge of a genomics-driven network trial. The data can be also a valuable resource as a validation cohort for Immunotherapy and Target therapy genomic biomarker research.

TMBleR: a bioinformatic tool to optimize TMB estimation and predictive power.

MOTIVATION: Tumor mutational burden (TMB) has been proposed as a predictive biomarker for immunotherapy response in cancer patients, as it is thought to enrich for tumors with high neoantigen load. TMB assessed by whole-exome sequencing is considered the gold standard but remains confined to research settings. In the clinical setting, targeted gene panels sampling various genomic sizes along with diverse strategies to estimate TMB were proposed and no real standard has emerged yet. RESULTS: We provide the community with TMBleR, a tool to measure the clinical impact of various strategies of panel-based TMB measurement. AVAILABILITY AND IMPLEMENTATION: R package and docker container (GPL-3 Open Source license): https://acc-bioinfo.github.io/TMBleR/. Graphical-user interface website: https://bioserver.ieo.it/shiny/app/tmbler. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Advances in immune therapies in hematological malignancies.

Immunotherapy in cancer takes advantage of the exquisite specificity, potency, and flexibility of the immune system to eliminate alien tumor cells. It involves strategies to activate the entire immune defense, by unlocking mechanisms developed by tumor cells to escape from surrounding immune cells, as well as engineered antibody and cellular therapies. What is important to note is that these are therapeutics with curative potential. The earliest example of immune therapy is allogeneic stem cell transplantation, introduced in 1957, which is still an important modality in hematology, most notably in myeloid malignancies. In this review, we discuss developmental trends of immunotherapy in hematological malignancies, focusing on some of the strategies that we believe will have the most impact on future clinical practice in this field. In particular, we delineate novel developments for therapies that have already been introduced into the clinic, such as immune checkpoint inhibition and chimeric antigen receptor T-cell therapies. Finally, we discuss the therapeutic potential of emerging strategies based on T-cell receptors and adoptive transfer of allogeneic natural killer cells.

Detection of clustered circulating tumour cells in early breast cancer.

Circulating tumour cell (CTC) clusters have been proposed to be major players in the metastatic spread of breast cancer, particularly during advanced disease stages. Yet, it is unclear whether or not they manifest in early breast cancer, as their occurrence in patients with metastasis-free primary disease has not been thoroughly evaluated. In this study, exploiting nanostructured titanium oxide-coated slides for shear-free CTC identification, we detect clustered CTCs in the curative setting of multiple patients with early breast cancer prior to surgical treatment, highlighting their presence already at early disease stages. These results spotlight an important aspect of metastasis biology and the possibility to intervene with anti-cluster therapeutics already during the early manifestation of breast cancer.

Obesity is a risk factor for acute promyelocytic leukemia: evidence from population and cross-sectional studies and correlation with FLT3 mutations and polyunsaturated fatty acid metabolism.

Obesity correlates with hematologic malignancies including leukemias, but risk of specific leukemia subtypes like acute promyelocytic leukemia and underlying molecular mechanisms are poorly understood. We explored multiple datasets for correlation between leukemia, body mass index (BMI) and molecular features. In a population-based study (n=5.2 million), we correlated BMI with promyelocytic leukemia, and other acute myeloid, lymphoid or other leukemias. In cross-sectional studies, we tested BMI deviation in promyelocytic leukemia trial cohorts from that expected based on national surveys. We explored The Cancer Genome Atlas for transcriptional signatures and mutations enriched in promyelocytic leukemia and/or obesity, and confirmed a correlation between body mass and FLT3 mutations in promyelocytic leukemia cohorts by logistic regression. In the population-based study, hazard ratio per 5 kg/m2 increase was: promyelocytic leukemia 1.44 (95%CI: 1.0-2.08), non-promyelocytic acute myeloid leukemias 1.17 (95%CI: 1.10-1.26), lymphoid leukemias 1.04 (95%CI: 1.0-1.09), other 1.10 (95%CI: 1.04-1.15). In cross-sectional studies, body mass deviated significantly from that expected (Italy: P<0.001; Spain: P=0.011; USA: P<0.001). Promyelocytic leukemia showed upregulation of polyunsaturated fatty acid metabolism genes. Odds of FLT3 mutations were higher in obese acute myeloid leukemias (odds ratio=2.4, P=0.007), whether promyelocytic or not, a correlation confirmed in the pooled promyelocytic leukemia cohorts (OR=1.22, 1.05-1.43 per 5 kg/m2). These results strengthen the evidence for obesity as a bona fide risk factor for myeloid leukemias, and in particular APL. FLT3 mutations and polyunsaturated fatty acid metabolism may play a previously under-appreciated role in obesity-associated leukemogenesis.

Meta-analysis of diagnostic performance of serological tests for SARS-CoV-2 antibodies up to 25 April 2020 and public health implications.

We reviewed the diagnostic accuracy of SARS-CoV-2 serological tests. Random-effects models yielded a summary sensitivity of 82% for IgM, and 85% for IgG and total antibodies. For specificity, the pooled estimate were 98% for IgM and 99% for IgG and total antibodies. In populations with ≤ 5% of seroconverted individuals, unless the assays have perfect (i.e. 100%) specificity, the positive predictive value would be ≤ 88%. Serological tests should be used for prevalence surveys only in hard-hit areas.

On-chip implementation of the probabilistic quantum optical state comparison amplifier.

Propagation losses in transmission media limit the transmission distance of optical signals. In the case where the signal is made up of quantum optical states, conventional deterministic optical amplification schemes cannot be used to increase the transmission distance as the copying of an arbitrary and unknown quantum state is forbidden. One strategy that can offset propagation loss is the use of probabilistic, or non-deterministic, amplification schemes - an example of which is the state comparison amplifier. Here we report a state comparison amplifier implemented in a compact, fiber-coupled femtosecond laser-written waveguide chip as opposed to the large, bulk-optical components of previous designs. This pathfinder on-chip implementation of the quantum amplifier has resulted in several performance improvements: the polarization integrity of the written waveguides has resulted in improved visibility of the amplifier interferometers; the potential of substantially-reduced losses throughout the amplifier configuration; and a more compact and environmentally-stable amplifier which is scalable to more complex networks.

A knowledge-based framework for the discovery of cancer-predisposing variants using large-scale sequencing breast cancer data.

BACKGROUND: The landscape of cancer-predisposing genes has been extensively investigated in the last 30 years with various methodologies ranging from candidate gene to genome-wide association studies. However, sequencing data are still poorly exploited in cancer predisposition studies due to the lack of statistical power when comparing millions of variants at once. METHOD: To overcome these power limitations, we propose a knowledge-based framework founded on the characteristics of known cancer-predisposing variants and genes. Under our framework, we took advantage of a combination of previously generated datasets of sequencing experiments to identify novel breast cancer-predisposing variants, comparing the normal genomes of 673 breast cancer patients of European origin against 27,173 controls matched by ethnicity. RESULTS: We detected several expected variants on known breast cancer-predisposing genes, like BRCA1 and BRCA2, and 11 variants on genes associated with other cancer types, like RET and AKT1. Furthermore, we detected 183 variants that overlap with somatic mutations in cancer and 41 variants associated with 38 possible loss-of-function genes, including PIK3CB and KMT2C. Finally, we found a set of 19 variants that are potentially pathogenic, negatively correlate with age at onset, and have never been associated with breast cancer. CONCLUSIONS: In this study, we demonstrate the usefulness of a genomic-driven approach nested in a classic case-control study to prioritize cancer-predisposing variants. In addition, we provide a resource containing variants that may affect susceptibility to breast cancer.

Increased BMI correlates with higher risk of disease relapse and differentiation syndrome in patients with acute promyelocytic leukemia treated with the AIDA protocols.

We investigated whether body mass index (BMI) correlates with distinct outcomes in newly diagnosed acute promyelocytic leukemia (APL). The study population included 144 patients with newly diagnosed and genetically confirmed APL consecutively treated at a single institution. All patients received All-trans retinoic acid and idarubicin according to the GIMEMA protocols AIDA-0493 and AIDA-2000. Outcome estimates according to the BMI were carried out together with multivariable analysis for the risk of relapse and differentiation syndrome. Fifty-four (37.5%) were under/normal weight (BMI < 25), whereas 90 (62.5%) patients were overweight/obese (BMI ≥ 25). An increased BMI was associated with older age (P < .0001) and male sex (P = .02). BMI was the most powerful predictor of differentiation syndrome in multivariable analysis (odds ratio = 7.24; 95% CI, 1.50-34; P = .014). After a median follow-up of 6 years, the estimated cumulative incidence of relapse at 5 years was 31.6% (95% CI, 22.7%-43.8%) in overweight/obese and 11.2% (95% CI, 5.3%-23.8%) in underweight/normal weight patients (P = .029). Multivariable analysis showed that BMI was an independent predictor of relapse (hazard ratio = 2.45, 95% CI, 1.00-5.99, in overweight/obese vs under/normal weight patients, P = .049). An increased BMI at diagnosis is associated with a higher risk of developing differentiation syndrome and disease relapse in APL patients treated with AIDA protocols.

High-Fat Diet Promotes Acute Promyelocytic Leukemia through PPARδ-Enhanced Self-renewal of Preleukemic Progenitors.

Risk and outcome of acute promyelocytic leukemia (APL) are particularly worsened in obese-overweight individuals, but the underlying molecular mechanism is unknown. In established mouse APL models (Ctsg-PML::RARA), we confirmed that obesity induced by high-fat diet (HFD) enhances leukemogenesis by increasing penetrance and shortening latency, providing an ideal model to investigate obesity-induced molecular events in the preleukemic phase. Surprisingly, despite increasing DNA damage in hematopoietic stem cells (HSC), HFD only minimally increased mutational load, with no relevant impact on known cancer-driving genes. HFD expanded and enhanced self-renewal of hematopoietic progenitor cells (HPC), with concomitant reduction in long-term HSCs. Importantly, linoleic acid, abundant in HFD, fully recapitulates the effect of HFD on the self-renewal of PML::RARA HPCs through activation of peroxisome proliferator-activated receptor delta, a central regulator of fatty acid metabolism. Our findings inform dietary/pharmacologic interventions to counteract obesity-associated cancers and suggest that nongenetic factors play a key role. PREVENTION RELEVANCE: Our work informs interventions aimed at counteracting the cancer-promoting effect of obesity. On the basis of our study, individuals with a history of chronic obesity may still significantly reduce their risk by switching to a healthier lifestyle, a concept supported by evidence in solid tumors but not yet in hematologic malignancies. See related Spotlight, p. 47.

Caloric restriction leads to druggable LSD1-dependent cancer stem cells expansion.

Caloric Restriction (CR) has established anti-cancer effects, but its clinical relevance and molecular mechanism remain largely undefined. Here, we investigate CR's impact on several mouse models of Acute Myeloid Leukemias, including Acute Promyelocytic Leukemia, a subtype strongly affected by obesity. After an initial marked anti-tumor effect, lethal disease invariably re-emerges. Initially, CR leads to cell-cycle restriction, apoptosis, and inhibition of TOR and insulin/IGF1 signaling. The relapse, instead, is associated with the non-genetic selection of Leukemia Initiating Cells and the downregulation of double-stranded RNA (dsRNA) sensing and Interferon (IFN) signaling genes. The CR-induced adaptive phenotype is highly sensitive to pharmacological or genetic ablation of LSD1, a lysine demethylase regulating both stem cells and dsRNA/ IFN signaling. CR + LSD1 inhibition leads to the re-activation of dsRNA/IFN signaling, massive RNASEL-dependent apoptosis, and complete leukemia eradication in ~90% of mice. Importantly, CR-LSD1 interaction can be modeled in vivo and in vitro by combining LSD1 ablation with pharmacological inhibitors of insulin/IGF1 or dual PI3K/MEK blockade. Mechanistically, insulin/IGF1 inhibition sensitizes blasts to LSD1-induced death by inhibiting the anti-apoptotic factor CFLAR. CR and LSD1 inhibition also synergize in patient-derived AML and triple-negative breast cancer xenografts. Our data provide a rationale for epi-metabolic pharmacologic combinations across multiple tumors.

Mapping functional to morphological variation reveals the basis of regional extracellular matrix subversion and nerve invasion in pancreatic cancer.

Intratumor morphological heterogeneity of pancreatic ductal adenocarcinoma (PDAC) predicts clinical outcomes but is only partially understood at the molecular level. To elucidate the gene expression programs underpinning intratumor morphological variation in PDAC, we investigated and deconvoluted at single cell level the molecular profiles of histologically distinct clusters of PDAC cells. We identified three major morphological and functional variants that co-exist in varying proportions in all PDACs, display limited genetic diversity, and are associated with a distinct organization of the extracellular matrix: a glandular variant with classical ductal features; a transitional variant displaying abortive ductal structures and mixed endodermal and myofibroblast-like gene expression; and a poorly differentiated variant lacking ductal features and basement membrane, and showing neuronal lineage priming. Ex vivo and in vitro evidence supports the occurrence of dynamic transitions among these variants in part influenced by extracellular matrix composition and stiffness and associated with local, specifically neural, invasion.

Embryonic stem cell-derived hemangioblasts remain epigenetically plastic and require PRC1 to prevent neural gene expression.

Many lineage-specific developmental regulator genes are transcriptionally primed in embryonic stem (ES) cells; RNA Pol(II) is bound at their promoters but is prevented from productive elongation by the activity of polycomb repressive complexes (PRC) 1 and 2. This epigenetically poised state is thought to enable ES cells to rapidly execute multiple differentiation programs and is recognized by a simultaneous enrichment for trimethylation of lysine 4 and trimethylation of lysine 27 of histone H3 (bivalent chromatin) across promoter regions. Here we show that the chromatin profile of this important cohort of genes is progressively modified as ES cells differentiate toward blood-forming precursors. Surprisingly however, neural specifying genes, such as Nkx2-2, Nkx2-9, and Sox1, remain bivalent and primed even in committed hemangioblasts, as conditional deletion of PRC1 results in overt and inappropriate expression of neural genes in hemangioblasts. These data reinforce the importance of PRC1 for normal hematopoietic differentiation and reveal an unexpected epigenetic plasticity of mesoderm-committed hemangioblasts.

Cytotoxic effect of (1-methyl-1H-imidazol-2-yl)-methanamine and its derivatives in Pt(II) complexes on human carcinoma cell lines: a comparative study with cisplatin.

The synthesis and pharmacological characterisation of (1-methyl-1H-imidazol-2-yl)-methanamine and its derivatives in Pt(II) complexes are described. Six out of eleven new Pt(II) complexes showed a significant cytotoxic effect on NCI-H460 lung cancer cell line with EC50 values between 1.1 and 0.115 mM, determined by MTT assay. Compound Pt-4a showed a particularly more potent cytotoxic effect than the previously described Pt(II) complex with 2,2'-bipyridine, [Pt(bpy)Cl2], with an EC50 value equal to 172.7 µM versus 726.5 µM respectively, and similar potency of cisplatin (EC50=78.3 µM) in NCI-H460 cell line. The determination of the intracellular and DNA-bound concentrations of (195)Pt, as marker of the presence of the complexes, showed that the cytotoxic compound Pt-4a readily diffused into the cells to a similar extent of cisplatin and directly interacted with the nuclear DNA. Pt-4a induced both p53 and p21(Waf) expression in NCI-H460 cells similar to cisplatin. A direct comparison of the cytotoxic effect between compound Pt-4a and cisplatin on 12 different cancer cell lines demonstrated that compound Pt-4a was in general less potent than cisplatin, but it had a comparable cytotoxic effect on non-small-cell lung cancer NCI-H460 cells, and the colorectal cancer cells HCT-15 and HCT-116. Altogether, these results suggested that the Pt(II) complex with 1-methyl-1H-imidazol-2-yl)-methanamine (compound Pt-4a), displayed a significant cytotoxic activity in cancer cells. Similarly to cisplatin this compound interacts with nuclear DNA and induces both p53 and p21(waf), and thus it represents an interesting starting point for future optimisation of new Pt(II) complexes forming DNA adducts.

The clinical impact of continued smoking in patients with breast and other hormone-dependent cancer: A systematic literature review.

We conducted a systematic review of studies that investigated whether quitting smoking at or around diagnosis improves survival of patients with hormone-dependent cancers (HDC). Nine studies published in 2013-2022 were included. Studies were very diverse in terms of design, definition of quitters and continued smokers, and prevalence of prognostic factors other than smoking cessation (e.g. patients' demographics, tumour characteristic, and treatments). For breast, ovarian, and endometrial cancer, all included studies found that quitters had better overall, disease specific, and disease-free survival than continued smokers. For prostate cancer, there was no evidence of an association of smoking cessation with improved survival. This literature review provided suggestive evidence that female smokers diagnosed with cancer of the breast, ovary, or endometrium may improve their chances of surviving by stopping smoking. Smoking cessation counselling should become part of standard oncological care for these patients and integrated into breast cancer screening programs.