u/RoughSympathy5644

I’m trying to work out whether I’m simply rediscovering an established concept under different terminology.

Several well-described observations seem to fit together:

- oncogene inhibition can cause survival and pro-death signals to decay at different rates (“oncogenic shock”);

- dynamic BH3 profiling can detect early treatment-induced changes in apoptotic priming;

- targeted therapy can rapidly induce adaptive rescue mechanisms, including pathway reactivation or new dependencies on anti-apoptotic proteins such as MCL-1 or BCL-xL.

This made me wonder whether treatment response in oncogene-addicted cancers could be viewed as a temporal competition between loss of survival signalling, apoptotic vulnerability, and adaptive rescue.

The hypothesis would be that sensitive tumours have a larger or longer transient vulnerability window after driver inhibition, while resistant tumours either fail to enter that state or escape from it more rapidly.

If so, I would expect:

1. early temporal features after driver inhibition to predict later cell death better than baseline pathway activity alone;

2. sensitive and resistant models to differ in the duration or magnitude of this transient state;

3. the optimal timing of a second intervention to depend on when adaptive rescue emerges, rather than necessarily favouring simultaneous combination treatment.

I realise that none of the individual mechanisms here are novel. What I have not been able to determine is whether they have already been explicitly integrated and tested as a general predictive framework based on temporal dynamics across oncogene-addicted cancers.

So I’d particularly appreciate input from people working in oncology/cancer biology:

Is this already a recognised framework under terminology I’m missing?

And if not:

What is the strongest biological reason to expect this model not to generalise?

I’m not an oncologist or cancer biologist, so I’m primarily looking for missing literature or a good reason to falsify the idea rather than trying to claim novelty.

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TL;DR: Could the response of oncogene-addicted cancers be predicted by the timing between loss of survival signalling, increased apoptotic vulnerability, and adaptive rescue after targeted therapy? I’m wondering whether this has already been tested as a general framework, rather than studied as several separate mechanisms.

I’m not a cancer researcher. I’m a physiotherapist with an academic background in psychology. This question emerged during an AI-assisted cross-domain brainstorming exercise, after which I checked the relevant oncology literature and tried to formulate the idea as a falsifiable hypothesis rather than a claim of discovery.

Background

Three established observations seem potentially connected.

1. Oncogenic shock

Oncogene-dependent tumour cells can receive both pro-survival and pro-death signals from the same oncogenic network. After acute inhibition of the driver, these signals may decay at different rates. If survival signalling disappears faster than pro-apoptotic signalling, a temporary state favouring cell death may emerge.

2. Dynamic apoptotic priming

Dynamic BH3 profiling has shown that treatment-induced changes in mitochondrial apoptotic priming can occur relatively early and can predict later treatment-induced cell death.

3. Adaptive rescue

Targeted therapy can also rapidly provoke compensatory responses. Tumour cells may reactivate alternative signalling pathways or become newly dependent on anti-apoptotic proteins such as MCL-1 or BCL-xL, potentially allowing them to escape the initial vulnerability created by driver inhibition.

Hypothesis

Could these processes be viewed as different parts of one temporal system?

My hypothesis is that treatment response in oncogene-addicted cancers may depend not only on the presence or baseline activity of an oncogenic driver, but on the relative timing of three processes after driver inhibition:

1. loss of oncogene-dependent survival signalling,

2. persistence or increase of apoptotic pressure,

3. emergence of compensatory survival mechanisms.

This could produce a transient vulnerability window whose magnitude and duration differ between tumours.

The relevant question might therefore not only be:

«How dependent is this tumour on EGFR, ALK, BRAF, KRAS, etc.?»

but also:

«What happens during the first minutes and hours after that dependency is disrupted, and how long does the cell remain apoptotically vulnerable before adaptive rescue occurs?»

Predictions

If this framework is correct, I would expect at least three things:

1. Treatment-sensitive tumour models should show a larger or longer temporal separation between loss of survival signalling and adaptive rescue than resistant models.

2. Features of this early time course, such as time to maximal apoptotic priming, rate of survival-signal decline, or delay until pathway reactivation, should predict later cell death better than baseline biomarkers alone.

3. The optimal timing of a second intervention should correspond to the measured vulnerability state rather than necessarily being simultaneous with the first treatment.

For example, if driver inhibition creates a secondary MCL-1 dependency several hours later, targeting MCL-1 at that point might differ biologically from administering both drugs simultaneously.

I also suspect that single-cell measurements would be important. A vulnerability window observed in bulk could otherwise simply represent different subpopulations following different trajectories, rather than individual cells passing through the same intermediate state.

What I’d like to know

The individual pieces here are obviously not new. Oncogenic shock, dynamic BH3 profiling, adaptive resistance, pathway reactivation, and treatment-induced BCL-2-family dependencies all have substantial existing literatures.

What I have not been able to determine is whether they have already been explicitly tested as a general predictive framework based on the relative temporal dynamics of survival, apoptotic vulnerability, and adaptive rescue across oncogene-addicted cancers.

So my main questions are:

Has this already been investigated explicitly in this form? If so, what terminology or literature should I be looking for?

And perhaps more importantly:

If it has not, what is the strongest biological or methodological reason why this framework would fail?

I’m much more interested in falsification, missing literature, or conceptual problems than in confirmation. If this is already a well-established framework under terminology I haven’t encountered, that would also be extremely useful to know.