Cigarette smoking is one of the leading causes of preventable human morbidity and mortality, causing serious diseases such as cardiovascular diseases, chronic obstructive pulmonary disease (COPD), and lung cancer. The vast majority of the smoking-related diseases are caused by the toxicants present in cigarette smoke (Farsalinos and Le Houezec, 2015), which are mostly formed during the combustion of tobacco¹. The United States Surgeon General has stated that the “burden of death and disease from tobacco use in the United States is overwhelmingly caused by cigarettes and other combusted tobacco products” (U.S. Department of Health and Human Services, 2014). Nicotine, while addictive, not risk-free, and an important factor in why people smoke, is not the primary cause of diseases (Tobacco Advisory Group of the Royal College of Physicians, 2000; Benowitz, 2010; U.S. Department of Health and Human Services, 2014).

For decades, the efforts to reduce the harm caused by smoking have been focused on preventing smoking initiation and promoting smoking cessation (Zhu et al., 2012). More recently, tobacco harm reduction (THR) has emerged as a third approach that can help to reduce the adverse effects of smoking. THR is based on switching consumers to less harmful products that emit significantly lower levels of toxicants while providing levels of nicotine comparable to cigarettes (Smith et al., 2016). As noted by McNeil, “Since nicotine itself is not a highly hazardous drug, encouraging smokers to obtain nicotine from sources that do not involve tobacco combustion is a potential means to reduce the morbidity and mortality they sustain, without the need to overcome their addiction to nicotine” (McNeil, 2012). This new approach complements those aimed at reducing smoking prevalence and aims to provide smokers who do not quit with novel tobacco or nicotine-containing products that are substantially less toxic than cigarettes. The United States Family Smoking Prevention and Tobacco Control Act embraces the concept of THR and defines a modified risk tobacco product (MRTP) as any tobacco product that is sold or distributed for use to reduce harm or the risk of tobacco related disease associated with commercially marketed tobacco products (Luke et al., 2011).

MRTPs are designed with the objective to significantly reduce or eliminate the emission of toxicants – so-called harmful and potentially harmful constituents (HPHC) – while preserving the taste, sensory experience, nicotine delivery profile, and ritual characteristics of cigarettes as much as possible. The aim of these products is to both reduce the risk of smoking-related disease and allow current adult smokers to switch to them. While still containing tobacco, novel heated tobacco products generally emit significantly lower levels of HPHCs than cigarettes (Tobacco Advisory Group of the Royal College of Physicians, 2016; Farsalinos et al., 2017). One such candidate MRTP is the Tobacco Heating System (THS) 2.2 developed by Philip Morris International, which has been launched in several European and Asian markets. Other similar products exist and are becoming an important part of current harm reduction strategies (Tobacco Advisory Group of the Royal College of Physicians, 2016; Murphy et al., 2017).

Tobacco Heating System 2.2 is a heat-not-burn product that heats tobacco at a temperature below that required to initiate combustion (Smith et al., 2016). THS 2.2 consists of three components: (1) a tobacco stick, a novel patent-pending tobacco product; (2) a holder, into which the tobacco stick is inserted, which heats the tobacco by means of an electronically controlled heating blade, and (3) a charger, which is used to recharge the holder after each use. To operate the THS 2.2, the user inserts a tobacco stick into the holder and turns on the device by means of a switch. This initiates the heating of the tobacco via the heating blade inserted into the tobacco plug. Heat is supplied to the tobacco stick for a fixed period of approximately 6 min and allows up to 14 puffs to be taken during that time. The temperature of the heating blade is controlled carefully, and the energy supply to the blade is cut if its operating temperature exceeds 350°C (Smith et al., 2016).

Tobacco Heating System 2.2, which was designed to heat rather than burn tobacco, emits > 90% lower levels of HPHCs than cigarettes (Forster et al., 2015; Schaller et al., 2016b; Bekki et al., 2017). As a consequence, smokers who switch completely to THS 2.2 use should be exposed to significantly lower levels of HPHCs.

To test this hypothesis, four randomized, controlled, open-label, three-arm parallel group clinical studies have been conducted to determine whether switching from smoking to THS 2.2 use reduces exposure to HPHCs and to compare the reductions in exposure with those observed when subjects quit for the duration of the studies. The exposure to HPHCs was quantified using Biomarkers of Exposure (BoExp) to specific smoke constituents, and the four studies demonstrated consistently that study participants who switched from cigarette smoking to THS 2.2 used were exposed to significantly lower levels of HPHCs than those who continued to smoke cigarettes. Furthermore, these studies showed that the effects of switching were similar to those caused by smoking abstinence (SA) (Haziza et al., 2016a,b; Ludicke et al., 2018a,b; Haziza et al., 2019).

While BoExp are valuable tools to quantify the body’s exposure to specific HPHCs, they do not capture the biological responses elicited by the exposure to HPHCs. Systems toxicology aims to achieve a global understanding of the exposure response and utilizes high-throughput technologies for extensive molecular measurements (Sturla et al., 2014; Hartung et al., 2017). In the context of smoking, gene expression profiling has revealed mechanistic insights into the disease-related changes that occur in the respiratory track of smokers (Spira et al., 2004, 2007). Gene expression profiling can also be used to develop signatures that are characteristic to certain subject groups and have predictive and prognostic value. A recent analysis of existing gene signatures for primary lung adenocarcinoma showed a significant classification agreement between the signatures (Ringnér and Staaf, 2016).

Even though it is not the primary tissue affected by smoking, whole blood is an attractive matrix for gene expression profiling. Blood is easy to acquire and reflects the smoking-induced changes in the systemic immune response (Jensen et al., 1998; Wang et al., 2013; Hoonhorst et al., 2014). The challenge is that the molecular changes are difficult to turn into mechanistic insights because of the heterogeneity of the cellular composition of whole blood. Therefore, the value of blood-based gene expression profiling resides in the genes, in which expression pattern is distinctively representative of an exposure or a disease. For example, blood-based gene signatures have been shown to reflect different lung pathologies in patients (Showe et al., 2009; Rotunno et al., 2011; Zander et al., 2011; Bloom et al., 2013; DePianto et al., 2014; Huang et al., 2015) and even toxicant exposures (LaBreche et al., 2011; Beineke et al., 2012; Joseph et al., 2013; Bushel et al., 2016).

We have recently developed a blood-based gene signature that can distinguish current smokers (CS) from either nonsmokers (NS) or former smokers (FS) with high specificity and sensitivity (Martin et al., 2015). This smoke exposure response signature (SERS) of 11 genes (LRRN3, SASH1, PALLD, RGL1, TNFRSF17, CDKN1C, IGJ, RRM2, ID3, SERPING1, and FUCA1) has since been verified in the systems biology verification Industrial Methodology for PROcess VErification in Research (sbv IMPROVER) Systems Toxicology Computational Challenge. The genes proposed by the best performers overlapped remarkably with our gene signature (Belcastro et al., 2017; Poussin et al., 2017). The SERS could distinguish CS from NS with remarkable accuracy (Martin et al., 2015). Furthermore, the prediction scores from the training and validation cohort showed a clear separation between the FS (NS, respectively) smokers and the CS, allowing for a quantitative monitoring of smoking exposure. In these studies, no separation between FS (at least one year after quitting smoking) and NS was observed (Belcastro et al., 2017; Poussin et al., 2017).

To test this quantitative monitoring of smoking exposure, the SERS was further applied to blood samples collected from one of our 5-day reduced exposure clinical studies (NCT01959932, ZRHR-REX-C-03-EU) (Haziza et al., 2016b; Martin et al., 2016). The results obtained for the 5-day cessation cohort samples confirmed that the signature scores could serve as a quantitative biomarker. Once applied to the samples from the associated 5-day THS 2.2 switching arm, the reduction in score was comparable (Martin et al., 2016). The signature performed remarkably well in predicting significant reductions in exposure response within just 5 days after subjects switched to THS 2.2 or abstained from smoking. In this study, continued smoking served as a negative control (no change expected), while smoking abstinence, which removes the exposure completely, served as a positive control.

Here we have included the three other clinical reduced exposure studies conducted with THS 2.2 (NCT01970982, NCT01970995, and NCT01989156) to test the performance of the SERS in smokers who switched to THS 2.2 or abstained. One of the studies was similar to that reported in Haziza et al. (2016b) with exposure for 5 days in confinement, and two others included an additional 85-day ambulatory period. The detailed presentation of the primary and secondary endpoints of the studies is available in the study publications (Haziza et al., 2016a,b, 2019; Ludicke et al., 2018a,b).

We derived prediction scores for all RNA samples from every study, arm, subject, and sampling time point (Figure 2) for which the array quality control criteria were met (ZRHR-REXC-03-EU: 268 out of 304, ZRHR-REXC-04-JP: 228 out of 302, ZRHM-REXA-07-JP: 378 out of 402, and ZRHM-REXA-08-US: 296 out of 320). For each sampling time point and each subject enrolled, the difference in score to baseline (Day 0) was computed (see Materials and Methods). This difference quantifies the reduction in score of the prediction model, moving away from the smoking status (CS). It was then normalized to the expected difference observed in the training dataset E-MTAB-5279 between a CS sample and an FS sample. The FS used in this training cohort had quit smoking for at least 2 years and had smoked at least 10 cigarettes daily for at least 3 years. To avoid this normalizing range to be too sensitive, the 90th quantile of the CS sample scores and 10th quantile of the FS scores were used. If no change is observed between Day 6 (Day 91, respectively) and baseline (Day 0), it will be close to zero, while it will approach –100% if the score at Day 6 (Day 91, respectively) is comparable to the (almost) maximal change observed between CS and FS.

For the confinement periods (up to Day 6), the average decreases for quitting were 18.1, 20, 12.3, and 12.6% for the ZRHR-REXC-03-EU, ZRHR-REXC-04-JP, ZRHM-REXA-07-JP, and ZRHM-REXA-08-US studies, respectively. For the longer period, 7.2 and 13.3% reductions were observed in ZRHM-REXA-07-JP and ZRHM-REXA-08-US, respectively (Figure 3).

The switching arm, in which subjects were asked to use THS 2.2 (or THS2.2M), exhibited decreases of 9.1, 10.2, 9.8, and 14.2% for the confinement period, while 9.4 and 19.5% reductions were observed when followed by the ambulatory period.

Over the different periods and studies, no significant changes were observed for the CS arm, as expected.

To summarize the observed changes, meta-analyses of the relative decrease were performed for each period and arm (Figure 4). The change in the SA arm is more important than in the THS 2.2 arm for the confinement period, while the opposite trend was observed in the longer period [confinement followed by ambulatory (see discussion)]. All the results indicate a significant decrease for both the THS 2.2 (p-value < 1e-4 for the confinement period, 0.004 for the longer period) and SA arms (p-value < 1e-4 for both periods) as opposed to the CS arm, which was not expected to exhibit any difference.

Tobacco Heating System 2.2 is a heated tobacco product that emits > 90% lower levels of HPHCs than cigarettes (Schaller et al., 2016b; Tobacco Advisory Group of the Royal College of Physicians, 2016; Farsalinos et al., 2017). This reduction in emission translates into a reduction in exposure among clinical study participants who switched to THS 2.2 compared with those who continued to smoke cigarettes in four clinical reduced exposure studies (Haziza et al., 2016a,b; Ludicke et al., 2018a,b). These reductions were similar to those caused by SA.

Here, we report on a meta-analysis of the performance of the blood-based SERS in the four clinical studies conducted to evaluate the HPHC exposure reduction in smokers who switched from cigarettes to THS 2.2. In a previous article, we showed that after only 5 days, the SERS was able to distinguish between study participants who switched to THS 2.2 and those who continued to smoke cigarettes. During this short period, while the SERS separated the THS 2.2 arm from the continuous cigarette smoking arm, it did not reach the low linear discriminant analysis (LDA) scores observed following long-term smoking cessation (Martin et al., 2015).

Surprisingly, the LDA values for the THS 2.2 users tended to be closer to the FS’ LDA values after the ambulatory period, compared with those who were in the SA arm, in studies ZRHM-REX-A-07 and ZRHM-REX-A-08. This was largely driven by the higher variability observed in the scores for the SA arm over the ambulatory period. The higher LDA score in the SA arms was likely due to non-compliance (i.e., cigarette use) during the ambulatory period.

Blood-based gene signatures, such as the one presented here, could provide a means to test compliance in clinical studies to obtain a reliable measure of reduced exposure during switching to MRTPs or SA. As Affymetrix microarray profiling is rather cost- and labor-intensive and not the most sensitive of available gene expression technologies, quantitative reverse transcription polymerase chain reaction could provide an alternative testing method that has already been tested and described for the gene signature in Martin et al. (2015). The blood-based gene signature could also be transferred to an easy-to-use and economic kit for smaller blood volumes to facilitate clinical use. Such kits and technologies already exist to evaluate radiation exposure (Lucas et al., 2014; Kim et al., 2015). These kits are simple to use at different medical sites and even by subjects in their own homes. Our blood signature is suitable for the development of a smoking status assay leveraging technology, such as the DxDirect^®, that enables low-cost, high-throughput testing from a few drops of stabilized blood, entirely omitting the RNA isolation step.

While the SERS can serve as a quantitative biomarker, the linearity with respect to the time from quitting smoking and the response score may not be guaranteed. Indeed, the reduction in scores for the SA arm after 91 days compared with the scores at Day 6 is roughly similar. This may be due to a lack of linearity, differences in the confinement and ambulatory settings, or a combination of both.

Nevertheless, gene expression-based signatures, as opposed to detecting a single metabolite from a given HPHC, are linked to the systemic response to exposure and could be able to capture longer term effects. The dynamic ranges observed from the training (E-MTAB-5279) (quitting time of at least 2 years) and validation cohorts (E-MTAB-5278) indicate that the half-life of the SERS biomarker is likely to be more than 3 months. This desirable feature for characterizing smoking exposure response reduction may reflect a slow change in the composition of the blood cell population between CS and NS. For example, LRRN3 is also expressed in T lymphocytes, and its high expression was shown to correlate with a less-senescent T cell phenotype (Chou et al., 2013).

To further unravel the functional relationship between quitting time and SERS scores, samples from an ongoing clinical study (registered in clinicaltrials.gov with the number NCT02432729), comprising a one-year quitting arm, will be leveraged.

To conclude, the small signature consisting of only 11 genes was applied on the blood transcriptome of subjects enrolled in four clinical studies. The resulting scores showed a consistent reduced exposure response in subjects who either stopped smoking or switched to THS 2.2 (including the mentholated version), compared with subjects who continued smoking cigarettes. With its remarkable capacity to indicate the smoking status of an individual, the signature could be further employed in the development of a low-cost and easy-to-use testing kit for small blood volumes.

MP and JH conceived the study. FM processed and analyzed the data and wrote the first draft of the manuscript. MT and CH wrote sections of the manuscript. NI managed the laboratory producing of the data. JH contributed to the writing of the manuscript and provided the resources. MP wrote the manuscript and supervised the work. All authors contributed to manuscript revision, read and approved the submitted version.

All authors are employees of Philip Morris International.